Recent Trends in Network Security - 2025
ijnsa
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Sep 02, 2025
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About This Presentation
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical...
Slide Content
Recent Trends in
Network Security -
2025
International Journal of Network
Security & Its Applications (IJNSA)
ERA Indexed
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
https://airccse.org/journal/ijnsa.html
Citations, h-index, i10-index
Citations 11802 h-index 51 i10-index 215
Network Security -
2025
International Journal of Network
Security & Its Applications (IJNSA)
ERA Indexed
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
https://airccse.org/journal/ijnsa.html
Citations, h-index, i10-index
Citations 11802 h-index 51 i10-index 215
SMART METER SECURITY ISSUES: A REVIEW PAPER
Osama Alshannaq
1
, Mohd Rizuan Baharon
1
, Shekh Faisal Abdul Latip
1
, Hairol Nizam Mohd
Shah
2
and Áine MacDermott
3
1
Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang
Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
2
Fakulti Teknologi & Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah
Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
3
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool,
L3 3AF, United Kingdom.
ABSTRACT
In recent decades, conventional electric power systems have seen escalating issues due to rising
electrical consumption, leading to voltage instability, recurrent blackouts, and heightened carbon
emissions. These challenges highlight the pressing necessity for a more efficient and sustainable
energy infrastructure. The smart grid has emerged as a disruptive solution, providing improved
energy distribution, real-time monitoring, and facilitating renewable integration. Central to this
evolution are smart meters, which are essential elements of the Advanced Metering
Infrastructure (AMI), facilitating precise energy monitoring, bidirectional connectivity, and
remote oversight within smart households and grids. Nonetheless, despite their functionalities,
smart meters provide potential security threats, including susceptibility to cyberattacks and
physical interference. This review article seeks to examine the fundamental characteristics and
functionalities of smart meters, identify significant security and implementation difficulties, and
emphasise their role within the larger smart grid ecosystem. This paper conducts a thorough
analysis of existing literature to analyse the communication technologies utilized, the possible
threat landscape, and the significance of strong security frameworks. The results underscore the
necessity for secure communication protocols, sophisticated encryption, and physical protections
to guarantee the reliability and integrity of smart meter implementations in contemporary power
systems.
KEYWORDS
Smart grid; Secure smart meter; Attacks on data; network attacks; Physical hardware attacks.
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa01.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
Osama Alshannaq
1
, Mohd Rizuan Baharon
1
, Shekh Faisal Abdul Latip
1
, Hairol Nizam Mohd
Shah
2
and Áine MacDermott
3
1
Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang
Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
2
Fakulti Teknologi & Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah
Jaya, 76100 Durian Tunggal, Melaka, Malaysia.
3
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool,
L3 3AF, United Kingdom.
ABSTRACT
In recent decades, conventional electric power systems have seen escalating issues due to rising
electrical consumption, leading to voltage instability, recurrent blackouts, and heightened carbon
emissions. These challenges highlight the pressing necessity for a more efficient and sustainable
energy infrastructure. The smart grid has emerged as a disruptive solution, providing improved
energy distribution, real-time monitoring, and facilitating renewable integration. Central to this
evolution are smart meters, which are essential elements of the Advanced Metering
Infrastructure (AMI), facilitating precise energy monitoring, bidirectional connectivity, and
remote oversight within smart households and grids. Nonetheless, despite their functionalities,
smart meters provide potential security threats, including susceptibility to cyberattacks and
physical interference. This review article seeks to examine the fundamental characteristics and
functionalities of smart meters, identify significant security and implementation difficulties, and
emphasise their role within the larger smart grid ecosystem. This paper conducts a thorough
analysis of existing literature to analyse the communication technologies utilized, the possible
threat landscape, and the significance of strong security frameworks. The results underscore the
necessity for secure communication protocols, sophisticated encryption, and physical protections
to guarantee the reliability and integrity of smart meter implementations in contemporary power
systems.
KEYWORDS
Smart grid; Secure smart meter; Attacks on data; network attacks; Physical hardware attacks.
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa01.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
REFERENCES
[1] Khan, R., et al., "Future Internet: The Internet of Things Architecture, Possible
Applications and Key Challenges," Proc. 10th Int. Conf. Frontiers of Information
Technology, 2021.
[2] Gungor, V. C., et al., "Smart Grid Technologies: Communication Technologies and
Standards," IEEE Transactions on Industrial Informatics, vol. 7, no. 4, pp. 529–539,
2021.
[3] C. Alcaraz and J. Lopez, "A security analysis for wireless sensor mesh networks in highly
critical systems," IEEE Trans. Syst., Man, Cybern., vol. 42, no. 4, pp. 939–950, 2022.
[4] W. Wang and Z. Lu, "Cyber security in the smart grid: Survey and challenges," Comput.
Netw., vol. 57, no. 5, pp. 1344–1371, 2023.
[5] Z. Tan et al., "Privacy-aware smart metering: Challenges and opportunities," IEEE
Commun. Surveys Tuts., vol. 25, no. 1, pp. 108–134, 2023.
[6] Y. Zou et al., "A survey on wireless security: Technical challenges, recent advances, and
future trends," Proc. IEEE, vol. 104, no. 9, pp. 1727–1765, 2022.
[7] N. Saxena and S. Grijalva, "Security and privacy issues in smart grid metering and
control," IEEE Commun. Mag., vol. 50, no. 5, pp. 38–45, 2023.
[8] H. Mohsenian-Rad and A. Leon-Garcia, "Distributed Internet-based load altering attacks
against smart power grids," IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 667–674, 2021.
[9] C. Efthymiou and G. Kalogridis, "Smart grid privacy via anonymization of smart
metering data," in Proc. IEEE SmartGridComm, 2022.
[10] A. Molina-Markham et al., "Private memoirs of a smart meter," in Proc. ACM
BuildSys, 2023.
[11] P. Kumar and H.-J. Lee, "Security issues in healthcare applications using wireless
medical sensor networks: A survey," Sensors, vol. 12, no. 1, pp. 55–91, 2022.
[12] A. Elmaghraby and M. Losavio, "Cyber security challenges in smart grid
systems," Comput. Netw., vol. 57, no. 5, pp. 1344–1371, 2023.
[13] W. Elmenreich and D. Egarter, "Design guidelines for smart appliances,"
in WISES 2012 – Proc. Workshop Intell. Solutions Embedded Syst., pp. 76–82, May
2012.
[14] F. Molazem, "Security and privacy of smart meters: A survey," Working Paper,
pp. 1–11, 2012.
[15] J. Zheng, D. W. Gao, and L. Lin, "Smart meters in smart grid: An overview,"
in IEEE Green Technol. Conf., pp. 57–64, 2013.
[16] F. Benzi, N. Anglani, E. Bassi, and L. Frosini, "Electricity smart meters
interfacing the households," IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4487–4494,
Oct. 2011.
[17] X. Fan and G. Gong, "Security challenges in smart-grid metering and control
systems," Technol. Innov. Manag. Rev., vol. 3, no. 7, pp. 12–18, 2013.
[18] D. von Oheimb, "IT security architecture approaches for smart metering and
smart grid," in Int. Workshop Smart Grid Security, Springer, pp. 1–25, 2012.
[19] S. Kaplantzis and Y. A. Şekercioğlu, "Security and smart metering," in Proc. Eur.
Wireless Conf., pp. 1–8, 2012.
[20] Z. Fan et al., "Smart grid communications: Overview of research challenges,
solutions, and standardization activities," IEEE Commun. Surveys Tuts., vol. 15, no. 1,
pp. 21–38, 2013.
[21] C. A. F. Lima and J. R. P. Navas, "Smart metering and systems to support a
conscious use of water and electricity," in Proc. 24th Int. Conf. ECOS, pp. 961–973,
2011.
[1] Khan, R., et al., "Future Internet: The Internet of Things Architecture, Possible
Applications and Key Challenges," Proc. 10th Int. Conf. Frontiers of Information
Technology, 2021.
[2] Gungor, V. C., et al., "Smart Grid Technologies: Communication Technologies and
Standards," IEEE Transactions on Industrial Informatics, vol. 7, no. 4, pp. 529–539,
2021.
[3] C. Alcaraz and J. Lopez, "A security analysis for wireless sensor mesh networks in highly
critical systems," IEEE Trans. Syst., Man, Cybern., vol. 42, no. 4, pp. 939–950, 2022.
[4] W. Wang and Z. Lu, "Cyber security in the smart grid: Survey and challenges," Comput.
Netw., vol. 57, no. 5, pp. 1344–1371, 2023.
[5] Z. Tan et al., "Privacy-aware smart metering: Challenges and opportunities," IEEE
Commun. Surveys Tuts., vol. 25, no. 1, pp. 108–134, 2023.
[6] Y. Zou et al., "A survey on wireless security: Technical challenges, recent advances, and
future trends," Proc. IEEE, vol. 104, no. 9, pp. 1727–1765, 2022.
[7] N. Saxena and S. Grijalva, "Security and privacy issues in smart grid metering and
control," IEEE Commun. Mag., vol. 50, no. 5, pp. 38–45, 2023.
[8] H. Mohsenian-Rad and A. Leon-Garcia, "Distributed Internet-based load altering attacks
against smart power grids," IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 667–674, 2021.
[9] C. Efthymiou and G. Kalogridis, "Smart grid privacy via anonymization of smart
metering data," in Proc. IEEE SmartGridComm, 2022.
[10] A. Molina-Markham et al., "Private memoirs of a smart meter," in Proc. ACM
BuildSys, 2023.
[11] P. Kumar and H.-J. Lee, "Security issues in healthcare applications using wireless
medical sensor networks: A survey," Sensors, vol. 12, no. 1, pp. 55–91, 2022.
[12] A. Elmaghraby and M. Losavio, "Cyber security challenges in smart grid
systems," Comput. Netw., vol. 57, no. 5, pp. 1344–1371, 2023.
[13] W. Elmenreich and D. Egarter, "Design guidelines for smart appliances,"
in WISES 2012 – Proc. Workshop Intell. Solutions Embedded Syst., pp. 76–82, May
2012.
[14] F. Molazem, "Security and privacy of smart meters: A survey," Working Paper,
pp. 1–11, 2012.
[15] J. Zheng, D. W. Gao, and L. Lin, "Smart meters in smart grid: An overview,"
in IEEE Green Technol. Conf., pp. 57–64, 2013.
[16] F. Benzi, N. Anglani, E. Bassi, and L. Frosini, "Electricity smart meters
interfacing the households," IEEE Trans. Ind. Electron., vol. 58, no. 10, pp. 4487–4494,
Oct. 2011.
[17] X. Fan and G. Gong, "Security challenges in smart-grid metering and control
systems," Technol. Innov. Manag. Rev., vol. 3, no. 7, pp. 12–18, 2013.
[18] D. von Oheimb, "IT security architecture approaches for smart metering and
smart grid," in Int. Workshop Smart Grid Security, Springer, pp. 1–25, 2012.
[19] S. Kaplantzis and Y. A. Şekercioğlu, "Security and smart metering," in Proc. Eur.
Wireless Conf., pp. 1–8, 2012.
[20] Z. Fan et al., "Smart grid communications: Overview of research challenges,
solutions, and standardization activities," IEEE Commun. Surveys Tuts., vol. 15, no. 1,
pp. 21–38, 2013.
[21] C. A. F. Lima and J. R. P. Navas, "Smart metering and systems to support a
conscious use of water and electricity," in Proc. 24th Int. Conf. ECOS, pp. 961–973,
2011.
[22] K. K. Karmakar, V. Varadharajan, and U. Tupakula, "Mitigating attacks in
software defined networks," Clust. Comput., vol. 22, pp. 1143–1157, 2019.
[23] Y. Liu, Y. Wang, and H. Feng, "POAGuard: A defense mechanism against
preemptive table overflow attack in software-defined networks," IEEE Access, vol. 11,
pp. 123659–123676, 2023.
[24] P. Kumar et al., "Smart grid metering networks: A survey on security, privacy and
open research issues," IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2886–2927,
2019.
[25] T. Zou et al., "Smart grids cyber-physical security: Parameter correction model
against unbalanced false data injection attacks," Electr. Power Syst. Res., vol. 187, p.
106490, 2020.
[26] R. Chen, X. Li, H. Zhong, and M. Fei, "A novel online detection method of data
injection attack against dynamic state estimation in smart grid," Neurocomputing, vol.
344, pp. 73–81, 2019.
[27] M. Z. Gunduz and R. Das, "Cyber-security on smart grid: Threats and potential
solutions," Comput. Netw., vol. 169, p. 107094, 2020.
[28] Y. Li et al., "Detection of false data injection attacks in smart grid: A secure
federated deep learning approach," IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4862–
4872, Nov. 2022.
[29] S. Kim et al., "A secure smart-metering protocol over power-line
communication," IEEE Trans. Power Del., vol. 26, no. 4, pp. 2370–2379, Oct. 2011.
[30] M. N. Al-Mhiqani et al., "Cyber-security incidents: A review of cases in cyber-
physical systems," Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 1, pp. 499–508, 2018.
[31] Y. Liu, Y. Wang, Y. Zhang, and J. Zhou, "Privacy-preserving multi-keyword
searchable encryption for smart grid edge computing," IEEE Trans. Ind. Informat., vol.
16, no. 3, pp. 2134–2144, 2020.
[32] A. Souror, M. M. Hassan, A. Alamri, and M. S. Hossain, "Efficient and privacy-
preserving searchable symmetric encryption for smart grid data," J. Supercomput., 2024.
doi: 10.1007/s11227-024-06326-z.
[33] H. Shen, Y. Liu, Z. Xia, and M. Zhang, "An efficient aggregation scheme
resisting on malicious data mining attacks for smart grid," Inf. Sci., vol. 526, pp. 289–
300, 2020.
[34] S. Zhang, J. Rong, and B. Wang, "A privacy protection scheme of smart meter for
decentralized smart home environment based on consortium blockchain," Int. J. Electr.
Power Energy Syst., vol. 121, p. 106140, 2020.
[35] M. Shrestha et al., "A methodology for security classification applied to smart
grid infrastructures," Int. J. Crit. Infrastruct. Prot., vol. 28, p. 100342, 2020.
[36] M. Erol-Kantarci and H. T. Mouftah, "Management of PHEV batteries in the
smart grid: Towards a cyber-physical power infrastructure," in Proc. IWCMC, pp. 795–
800, 2011.
[37] F. Li, B. Luo, and P. Liu, "Secure information aggregation for smart grids using
homomorphic encryption," in Proc. IEEE SmartGridComm, pp. 327–332, 2010.
[38] J. Liu et al., "Cyber security and privacy issues in smart grids," IEEE Commun.
Surveys Tuts., vol. 14, no. 4, pp. 981–997, 2012.
[39] M. Attia et al., "An efficient intrusion detection system against cyber-physical
attacks in the smart grid," Comput. Electr. Eng., vol. 68, pp. 499–512, 2018.
[40] O. Alshannaq et al., "Analysis of the lowest memory consumption through
running different cryptography techniques for different types of images," J. Phys. Conf.
Ser., vol. 2319, no. 1, p. 012027, 2022.
software defined networks," Clust. Comput., vol. 22, pp. 1143–1157, 2019.
[23] Y. Liu, Y. Wang, and H. Feng, "POAGuard: A defense mechanism against
preemptive table overflow attack in software-defined networks," IEEE Access, vol. 11,
pp. 123659–123676, 2023.
[24] P. Kumar et al., "Smart grid metering networks: A survey on security, privacy and
open research issues," IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2886–2927,
2019.
[25] T. Zou et al., "Smart grids cyber-physical security: Parameter correction model
against unbalanced false data injection attacks," Electr. Power Syst. Res., vol. 187, p.
106490, 2020.
[26] R. Chen, X. Li, H. Zhong, and M. Fei, "A novel online detection method of data
injection attack against dynamic state estimation in smart grid," Neurocomputing, vol.
344, pp. 73–81, 2019.
[27] M. Z. Gunduz and R. Das, "Cyber-security on smart grid: Threats and potential
solutions," Comput. Netw., vol. 169, p. 107094, 2020.
[28] Y. Li et al., "Detection of false data injection attacks in smart grid: A secure
federated deep learning approach," IEEE Trans. Smart Grid, vol. 13, no. 6, pp. 4862–
4872, Nov. 2022.
[29] S. Kim et al., "A secure smart-metering protocol over power-line
communication," IEEE Trans. Power Del., vol. 26, no. 4, pp. 2370–2379, Oct. 2011.
[30] M. N. Al-Mhiqani et al., "Cyber-security incidents: A review of cases in cyber-
physical systems," Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 1, pp. 499–508, 2018.
[31] Y. Liu, Y. Wang, Y. Zhang, and J. Zhou, "Privacy-preserving multi-keyword
searchable encryption for smart grid edge computing," IEEE Trans. Ind. Informat., vol.
16, no. 3, pp. 2134–2144, 2020.
[32] A. Souror, M. M. Hassan, A. Alamri, and M. S. Hossain, "Efficient and privacy-
preserving searchable symmetric encryption for smart grid data," J. Supercomput., 2024.
doi: 10.1007/s11227-024-06326-z.
[33] H. Shen, Y. Liu, Z. Xia, and M. Zhang, "An efficient aggregation scheme
resisting on malicious data mining attacks for smart grid," Inf. Sci., vol. 526, pp. 289–
300, 2020.
[34] S. Zhang, J. Rong, and B. Wang, "A privacy protection scheme of smart meter for
decentralized smart home environment based on consortium blockchain," Int. J. Electr.
Power Energy Syst., vol. 121, p. 106140, 2020.
[35] M. Shrestha et al., "A methodology for security classification applied to smart
grid infrastructures," Int. J. Crit. Infrastruct. Prot., vol. 28, p. 100342, 2020.
[36] M. Erol-Kantarci and H. T. Mouftah, "Management of PHEV batteries in the
smart grid: Towards a cyber-physical power infrastructure," in Proc. IWCMC, pp. 795–
800, 2011.
[37] F. Li, B. Luo, and P. Liu, "Secure information aggregation for smart grids using
homomorphic encryption," in Proc. IEEE SmartGridComm, pp. 327–332, 2010.
[38] J. Liu et al., "Cyber security and privacy issues in smart grids," IEEE Commun.
Surveys Tuts., vol. 14, no. 4, pp. 981–997, 2012.
[39] M. Attia et al., "An efficient intrusion detection system against cyber-physical
attacks in the smart grid," Comput. Electr. Eng., vol. 68, pp. 499–512, 2018.
[40] O. Alshannaq et al., "Analysis of the lowest memory consumption through
running different cryptography techniques for different types of images," J. Phys. Conf.
Ser., vol. 2319, no. 1, p. 012027, 2022.
[41] Fedorchenko, I., Oliinyk, A., Stepanenko, A., Zaiko, T., Korniienko, S., Burtsev,
N. "Development of a genetic algorithm for placing power supply sources in a distributed
electric network". European Journal of Enterprise Technologies, issue 5/101, 6–16
(2019).
[42] B. Stelte and G. D. Rodosek, “Thwarting attacks on ZigBee - Removal of the
KillerBee stinger,” 2013 9th International Conference on Network and Service
Management, CNSM 2013 and its three collocated Workshops - ICQT 2013, SVM 2013
and SETM 2013. pp. 219–226, 2013, doi: 10.1109/CNSM.2013.6727840.
[43] T. M. Chen, J. C. Sanchez-Aarnoutse, and J. Buford, “Petri Net Modeling of
Cyber-Physical Attacks on Smart Grid.” IEEE Transactions on Smart Grid, vol. 2, no. 4,
pp. 741-749, Dec. 2011, doi 10.1109/TSG.2011.2160000, 2011.
[44] Vigo R, Yüksel E, Ramli CD. Smart grid security a smart meter-centric
perspective. In2012 20th Telecommunications forum (TELFOR) 2012 Nov 20 (pp. 127-
130). IEEE.
[45] Haq EU, Pei C, Zhang R, Jianjun H, Ahmad F. Electricity-theft detection for
smart grid security using smart meter data: A deep-CNN based approach. Energy
Reports. 2023 Mar 1;9:634-43.
[46] Win, Lae Lae, and Samet Tonyalı. "Security and privacy challenges, solutions,
and open issues in smart metering: A review." 2021 6th International Conference on
Computer Science and Engineering (UBMK). IEEE, 2021.
[47] T. Goodspeed, S. Bratus, R. Melgares, R. Speers, and S. W. Smith, “Tools for
exploring the wireless attack surface in smart meters.” 2012 45th Hawaii International
Conference on System Sciences, 2012.
[48] C. Bennett and S. B. Wicker, “Decreased time delay and security enhancement
recommendations for AMI smart meter networks,” Innovative Smart Grid Technologies
Conference, ISGT 2010. 2010, doi: 10.1109/ISGT.2010.5434780.
[49] Oliinyk, A., Fedorchenko, I., Stepanenko, A., Katschan, A., Fedorchenko, Y.,
Kharchenko, A., Goncharenko, D. "Development of genetic methods for predicting the
incidence of volumes of emissions of pollutants in air". 2019 2nd International Workshop
on Informatics and Data-Driven Medicine, IDDM, CEUR Workshop Proceedings, 2019,
Vol.2488, pp. 340–353.
[50] P. Singh et al., "Blockchain and homomorphic encryption-based privacy-
preserving data aggregation model in smart grid," Computers & Electrical Engineering,
vol. 93, p. 107209, 2021.
[51] F. Naseri et al., "Cyber-physical cloud battery management systems: Review of
security aspects," Batteries, vol. 9, no. 7, p. 382, 2023.
[52] L. Alabdulkarim and Z. Lukszo, “Information security assurance in critical
infrastructures: Smart Metering case,” 2008 1st International Conference on
Infrastructure Systems and Services: Building Networks for a Brighter Future, INFRA
2008. 2008, doi: 10.1109/INFRA.2008.5439670.
[53] Oliinyk, A., Fedorchenko, I., Stepanenko, .Rud M., Goncharenko, D.
Implementation of evolutionary methods of solving the traveling salesman problem in a
robotic warehouse // Lecture Notes on Data Engineering and Communications
Technologies, 2021, 48, P. 263–292.
[54] W. Duo, M. Zhou, and A. Abusorrah, "A survey of cyber attacks on cyber
physical systems: Recent advances and challenges," IEEE/CAA J. Autom. Sinica, vol. 9,
no. 5, pp. 784–800, 2022.
[55] M. H. Yaghmaee, Q. A. Frugh, and M. Bahekmat, “Monitoring approach for
detection compromise attacks in smart meter,” IET Conference Publications, vol. 2013,
no. 615 CP. 2013, doi: 10.1049/cp.2013.0962.
N. "Development of a genetic algorithm for placing power supply sources in a distributed
electric network". European Journal of Enterprise Technologies, issue 5/101, 6–16
(2019).
[42] B. Stelte and G. D. Rodosek, “Thwarting attacks on ZigBee - Removal of the
KillerBee stinger,” 2013 9th International Conference on Network and Service
Management, CNSM 2013 and its three collocated Workshops - ICQT 2013, SVM 2013
and SETM 2013. pp. 219–226, 2013, doi: 10.1109/CNSM.2013.6727840.
[43] T. M. Chen, J. C. Sanchez-Aarnoutse, and J. Buford, “Petri Net Modeling of
Cyber-Physical Attacks on Smart Grid.” IEEE Transactions on Smart Grid, vol. 2, no. 4,
pp. 741-749, Dec. 2011, doi 10.1109/TSG.2011.2160000, 2011.
[44] Vigo R, Yüksel E, Ramli CD. Smart grid security a smart meter-centric
perspective. In2012 20th Telecommunications forum (TELFOR) 2012 Nov 20 (pp. 127-
130). IEEE.
[45] Haq EU, Pei C, Zhang R, Jianjun H, Ahmad F. Electricity-theft detection for
smart grid security using smart meter data: A deep-CNN based approach. Energy
Reports. 2023 Mar 1;9:634-43.
[46] Win, Lae Lae, and Samet Tonyalı. "Security and privacy challenges, solutions,
and open issues in smart metering: A review." 2021 6th International Conference on
Computer Science and Engineering (UBMK). IEEE, 2021.
[47] T. Goodspeed, S. Bratus, R. Melgares, R. Speers, and S. W. Smith, “Tools for
exploring the wireless attack surface in smart meters.” 2012 45th Hawaii International
Conference on System Sciences, 2012.
[48] C. Bennett and S. B. Wicker, “Decreased time delay and security enhancement
recommendations for AMI smart meter networks,” Innovative Smart Grid Technologies
Conference, ISGT 2010. 2010, doi: 10.1109/ISGT.2010.5434780.
[49] Oliinyk, A., Fedorchenko, I., Stepanenko, A., Katschan, A., Fedorchenko, Y.,
Kharchenko, A., Goncharenko, D. "Development of genetic methods for predicting the
incidence of volumes of emissions of pollutants in air". 2019 2nd International Workshop
on Informatics and Data-Driven Medicine, IDDM, CEUR Workshop Proceedings, 2019,
Vol.2488, pp. 340–353.
[50] P. Singh et al., "Blockchain and homomorphic encryption-based privacy-
preserving data aggregation model in smart grid," Computers & Electrical Engineering,
vol. 93, p. 107209, 2021.
[51] F. Naseri et al., "Cyber-physical cloud battery management systems: Review of
security aspects," Batteries, vol. 9, no. 7, p. 382, 2023.
[52] L. Alabdulkarim and Z. Lukszo, “Information security assurance in critical
infrastructures: Smart Metering case,” 2008 1st International Conference on
Infrastructure Systems and Services: Building Networks for a Brighter Future, INFRA
2008. 2008, doi: 10.1109/INFRA.2008.5439670.
[53] Oliinyk, A., Fedorchenko, I., Stepanenko, .Rud M., Goncharenko, D.
Implementation of evolutionary methods of solving the traveling salesman problem in a
robotic warehouse // Lecture Notes on Data Engineering and Communications
Technologies, 2021, 48, P. 263–292.
[54] W. Duo, M. Zhou, and A. Abusorrah, "A survey of cyber attacks on cyber
physical systems: Recent advances and challenges," IEEE/CAA J. Autom. Sinica, vol. 9,
no. 5, pp. 784–800, 2022.
[55] M. H. Yaghmaee, Q. A. Frugh, and M. Bahekmat, “Monitoring approach for
detection compromise attacks in smart meter,” IET Conference Publications, vol. 2013,
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Ultra Wide Band Antenna for Chipless RFID Tags” International Journal of Advanced
Computer Science and Applications(IJACSA), 12(4), 2021.
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TIME ELECTRICITY MARKET.” 2011 IEEE International Conference on Acoustics,
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Development of Security Systems Using Motion Sensor Powered by RF Energy
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[65] Alshannaq O, Baharon MR, Alsayaydeh JA, Hammouda MB, Hammouda K,
Nawafleh MM, Rahman AI. Analysis of the Lowest Memory Consumption (Memory
Usage) Through Running Different Cryptography Techniques for Different Types of
Images. InJournal of Physics: Conference Series 2022 Aug 1 (Vol. 2319, No. 1, p.
012027). IOP Publishing.
[66] Fedorchenko, I., Oliinyk, A., Stepanenko, Zaiko, T., Korniienko S., Kharchenko,
A. Construction of a genetic method to forecast the population health indicators based on
neural network models // Eastern-European Journal of Enterprise Technologies, 2020, 1
(4-103), P. 52–63. DOI: 10.15587/1729-4061.2020.197319.
[67] Alshannaq O, Alsayaydeh JA, Hammouda MB, Ali MF, Alkhashaab MA, Zainon
M, Jaya AS. Particle swarm optimization algorithm to enhance the roughness of thin film
in TiN coatings. ARPN Journal of Engineering and Applied Sciences. 2022;17(22):186-
93.
[68] I. Fedorchenko, A. Oliinyk, Jamil Abedalrahim Jamil Alsayaydeh, A.
Kharchenko, A. Stepanenko and V. Shkarupylo. MODIFIED GENETIC ALGORITHM
TO DETERMINE THE LOCATION OF THE DISTRIBUTION POWER SUPPLY
NETWORKS IN THE CITY. ARPN Journal of Engineering and Applied Sciences, 2020,
15(23), pp. 2850–2867.
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data analytics: Applications, methodologies, and challenges." IEEE Transactions on
Smart Grid 10, no. 3 (2018): 3125-3148.
[75] Islam, Shama Naz, Zubair Baig, and Sherali Zeadally. "Physical layer security for
the smart grid: Vulnerabilities, threats, and countermeasures." IEEE Transactions on
Industrial Informatics 15, no. 12 (2019): 6522-6530..
[76] Abbasinezhad-Mood, Dariush, Arezou Ostad-Sharif, Morteza Nikooghadam, and
Sayyed Majid Mazinani. "A secure and efficient key establishment scheme for
communications of smart meters and service providers in smart grid." IEEE Transactions
on Industrial Informatics 16, no. 3 (2019): 1495-1502.
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based provenance for advanced metering infrastructure." IEEE Access 7 (2019): 87345-
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Umang Agarwal, Manu Maheshwari, Soumyajit Dey, and Debdeep Mukhopadhyay.
"Safe is the new smart: PUF-based authentication for load modification-resistant smart
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Das, Sergey Kozlov, and Petar Solic. "A new IoT‐based smart energy meter for smart
grids." International Journal of Energy Research 45, no. 1 (2021): 189-202.
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Liu. "Intrusion detection for cybersecurity of smart meters." IEEE Transactions on Smart
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[81] Sureshkumar, Venkatasamy, S. Anandhi, Ruhul Amin, N. Selvarajan, and R.
Madhumathi. "Design of robust mutual authentication and key establishment security
protocol for cloud-enabled smart grid communication." IEEE Systems Journal 15, no. 3
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demand shaping, and load scheduling." IET Smart Grid 3, no. 5 (2020): 605-613..
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smart meter for decentralized smart home environment based on consortium blockchain."
International Journal of Electrical Power & Energy Systems 121 (2020): 106140.
[84] Shrestha, Manish, Christian Johansen, Josef Noll, and Davide Roverso. "A
methodology for security classification applied to smart grid infrastructures."
International Journal of Critical Infrastructure Protection 28 (2020): 100342.
(IMCOM) 2019 13 (pp. 554-562). Springer International Publishing.
[70] Kumar, P., Lin, Y., Bai, G., Paverd, A., Dong, J.S. and Martin, A., 2019. Smart
grid metering networks: A survey on security, privacy and open research issues. IEEE
Communications Surveys & Tutorials, 21(3), pp.2886-2927.
[71] Abdalzaher, Mohamed S., Mostafa M. Fouda, and Mohamed I. Ibrahem. "Data
privacy preservation and security in smart metering systems." Energies 15, no. 19 (2022):
7419..
[72] Garg, Sahil, Kuljeet Kaur, Georges Kaddoum, Joel JPC Rodrigues, and Mohsen
Guizani. "Secure and lightweight authentication scheme for smart metering infrastructure
in smart grid." IEEE Transactions on Industrial Informatics 16, no. 5 (2019): 3548-3557.
[73] Orlando M, Estebsari A, Pons E, Pau M, Quer S, Poncino M, Bottaccioli L, Patti
E. A smart meter infrastructure for smart grid IoT applications. IEEE Internet of Things
Journal. 2021 Dec 22;9(14):12529-41.
[74] Wang, Yi, Qixin Chen, Tao Hong, and Chongqing Kang. "Review of smart meter
data analytics: Applications, methodologies, and challenges." IEEE Transactions on
Smart Grid 10, no. 3 (2018): 3125-3148.
[75] Islam, Shama Naz, Zubair Baig, and Sherali Zeadally. "Physical layer security for
the smart grid: Vulnerabilities, threats, and countermeasures." IEEE Transactions on
Industrial Informatics 15, no. 12 (2019): 6522-6530..
[76] Abbasinezhad-Mood, Dariush, Arezou Ostad-Sharif, Morteza Nikooghadam, and
Sayyed Majid Mazinani. "A secure and efficient key establishment scheme for
communications of smart meters and service providers in smart grid." IEEE Transactions
on Industrial Informatics 16, no. 3 (2019): 1495-1502.
[77] Kamal, Mohsin, and Muhammad Tariq. "Light-weight security and blockchain-
based provenance for advanced metering infrastructure." IEEE Access 7 (2019): 87345-
87356
[78] Harishma, Boyapally, Paulson Mathew, Sikhar Patranabis, Urbi Chatterjee,
Umang Agarwal, Manu Maheshwari, Soumyajit Dey, and Debdeep Mukhopadhyay.
"Safe is the new smart: PUF-based authentication for load modification-resistant smart
meters." IEEE Transactions on Dependable and Secure Computing 19, no. 1 (2020): 663-
680.
[79] Avancini, Danielly B., Joel JPC Rodrigues, Ricardo AL Rabêlo, Ashok Kumar
Das, Sergey Kozlov, and Petar Solic. "A new IoT‐based smart energy meter for smart
grids." International Journal of Energy Research 45, no. 1 (2021): 189-202.
[80] Sun, Chih-Che, D. Jonathan Sebastian Cardenas, Adam Hahn, and Chen-Ching
Liu. "Intrusion detection for cybersecurity of smart meters." IEEE Transactions on Smart
Grid 12, no. 1 (2020): 612-622.
[81] Sureshkumar, Venkatasamy, S. Anandhi, Ruhul Amin, N. Selvarajan, and R.
Madhumathi. "Design of robust mutual authentication and key establishment security
protocol for cloud-enabled smart grid communication." IEEE Systems Journal 15, no. 3
(2020): 3565-3572.
[82] Farokhi, Farhad. "Review of results on smart‐meter privacy by data manipulation,
demand shaping, and load scheduling." IET Smart Grid 3, no. 5 (2020): 605-613..
[83] Zhang, Shaomin, Jieqi Rong, and Baoyi Wang. "A privacy protection scheme of
smart meter for decentralized smart home environment based on consortium blockchain."
International Journal of Electrical Power & Energy Systems 121 (2020): 106140.
[84] Shrestha, Manish, Christian Johansen, Josef Noll, and Davide Roverso. "A
methodology for security classification applied to smart grid infrastructures."
International Journal of Critical Infrastructure Protection 28 (2020): 100342.
[85] Chakraborty, Soham, Sarasij Das, Tarlochan Sidhu, and A. K. Siva. "Smart
meters for enhancing protection and monitoring functions in emerging distribution
systems." International Journal of Electrical Power & Energy Systems 127 (2021):
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106626.
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Protection in Smart Grid." International Artificial Intelligence Conference. Singapore:
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1170–1181, 2021.
AUTHORS
Osama Alshannaq received his Bachelor's degree in Computer Science at the
Faculty of Computer Science in Jordan at Jadara University in 2016. He did his
Master's degree in Computer Science (Internetworking technology) at the
Faculty of Information and Communication Technology, Universiti Teknikal
Malaysia Melaka (UTeM) in 2019. He is a Lecturer at the Department of
information technology and security, Toledo College, Jordan. He started his
career as a lecturer at this college in May 2021. He has experience in teaching Computer Science
and cryptography. His research interests are in the areas of Computer Networks, Computer
Science, Network Security, Data Security, Data Privacy and Integrity, and Cryptography.
Currently, he is doing his Ph.D. in the network security department at Universiti Teknikal
Malaysia Melaka (UTeM). He can be contacted at email: [email protected].
Mohd Rizuan Baharon received the PhD degree in Computer Science from
Liverpool John Moores University, Liverpool, United Kingdom, in 2017. He
completed his master degree in Mathematics in 2006 and his undergraduate
studies in 2004 at Universiti Teknologi Malaysia, Malaysia. Currently, he is a
Senior Lecturer at the Department of Computer System and Communication,
Faculty of Information and Communication Technology, Universiti Teknikal
Malaysia Melaka, Malaysia. He started his career as a lecturer at this university since June 2006.
He has vast experience in teaching Computer Science, Cryptography and Mathematics subjects.
His research interests are mainly in the area of Mobile Network Security, Cloud Computing
Security, Data Privacy and Integrity, Mobile Users Accountability and Cryptography. He is a
lifetime member of Mathematical and Sciences Association Malaysia (PERSAMA, Malaysia).
He has produced a number of journal and conference papers at national and international levels.
He can be contacted at email: [email protected]
Shekh Faisal Abdul Latip is currently working at the Faculty of Information
and Communication Technology, Universiti Teknikal Malaysia Melaka
(UTeM). He received his PhD in 2012 from the University of Wollongong,
Australia, in the field of Cryptography. Prior to his PhD studies, he obtained an
M.Sc in Information Security from Royal Holloway, University of London, in
2003. He earned both a B.Sc (Hons) in Computer Science (2000) and a
Diploma in Electronic Engineering (1994) from Universiti Teknologi Malaysia (UTM). His main
research interest focuses on symmetric-key cryptography, specifically the design and
cryptanalysis of block ciphers, stream ciphers, hash functions, and MACs. He is currently a
member of a focus group and one of the evaluation panel experts for the MySEAL project, which
aims to recommend a list of trusted cryptographic algorithms for use by public and private
sectors in Malaysia. To promote new ideas and activities in cryptology-related areas in Malaysia,
he joined and became a member of the executive committee of the Malaysian Society for
Cryptology Research (MSCR).
Osama Alshannaq received his Bachelor's degree in Computer Science at the
Faculty of Computer Science in Jordan at Jadara University in 2016. He did his
Master's degree in Computer Science (Internetworking technology) at the
Faculty of Information and Communication Technology, Universiti Teknikal
Malaysia Melaka (UTeM) in 2019. He is a Lecturer at the Department of
information technology and security, Toledo College, Jordan. He started his
career as a lecturer at this college in May 2021. He has experience in teaching Computer Science
and cryptography. His research interests are in the areas of Computer Networks, Computer
Science, Network Security, Data Security, Data Privacy and Integrity, and Cryptography.
Currently, he is doing his Ph.D. in the network security department at Universiti Teknikal
Malaysia Melaka (UTeM). He can be contacted at email: [email protected].
Mohd Rizuan Baharon received the PhD degree in Computer Science from
Liverpool John Moores University, Liverpool, United Kingdom, in 2017. He
completed his master degree in Mathematics in 2006 and his undergraduate
studies in 2004 at Universiti Teknologi Malaysia, Malaysia. Currently, he is a
Senior Lecturer at the Department of Computer System and Communication,
Faculty of Information and Communication Technology, Universiti Teknikal
Malaysia Melaka, Malaysia. He started his career as a lecturer at this university since June 2006.
He has vast experience in teaching Computer Science, Cryptography and Mathematics subjects.
His research interests are mainly in the area of Mobile Network Security, Cloud Computing
Security, Data Privacy and Integrity, Mobile Users Accountability and Cryptography. He is a
lifetime member of Mathematical and Sciences Association Malaysia (PERSAMA, Malaysia).
He has produced a number of journal and conference papers at national and international levels.
He can be contacted at email: [email protected]
Shekh Faisal Abdul Latip is currently working at the Faculty of Information
and Communication Technology, Universiti Teknikal Malaysia Melaka
(UTeM). He received his PhD in 2012 from the University of Wollongong,
Australia, in the field of Cryptography. Prior to his PhD studies, he obtained an
M.Sc in Information Security from Royal Holloway, University of London, in
2003. He earned both a B.Sc (Hons) in Computer Science (2000) and a
Diploma in Electronic Engineering (1994) from Universiti Teknologi Malaysia (UTM). His main
research interest focuses on symmetric-key cryptography, specifically the design and
cryptanalysis of block ciphers, stream ciphers, hash functions, and MACs. He is currently a
member of a focus group and one of the evaluation panel experts for the MySEAL project, which
aims to recommend a list of trusted cryptographic algorithms for use by public and private
sectors in Malaysia. To promote new ideas and activities in cryptology-related areas in Malaysia,
he joined and became a member of the executive committee of the Malaysian Society for
Cryptology Research (MSCR).
Hairol Nizam Mohd Shah received the Diploma (Electric-Electronic) from
Universiti Teknologi Malaysia in 2000, B.Eng. (Electric-Electronic) from
Universiti Malaysia Sabah in 2004. He received the M. Eng. (Mechatronics)
and PhD (Mechatronics) at Universiti Teknikal Malaysia Melaka in 2008 and
2018. Currently he is a senior lecturer at the Universiti Teknikal Malaysia
Melaka, Ayer Keroh Melaka. His primary interests related to vision systems,
robotics and computer-integrated and image processing.
Dr Áine MacDermott is a Senior Lecturer in the School of Computer Science
and Mathematics at Liverpool John Moores University (LJMU) in the UK. She
obtained her PhD in Network Security from LJMU in 2017, and a BSc (Hons)
in Computer Forensics in 2011. Áine is a member of Research Centre for
Critical Infrastructure Computer Technology and Protection (PROTECT) at
LJMU, with research interests including the Internet of Things, collaborative
intrusion detection in interconnected networks, digital forensics, and machine learning.
Universiti Teknologi Malaysia in 2000, B.Eng. (Electric-Electronic) from
Universiti Malaysia Sabah in 2004. He received the M. Eng. (Mechatronics)
and PhD (Mechatronics) at Universiti Teknikal Malaysia Melaka in 2008 and
2018. Currently he is a senior lecturer at the Universiti Teknikal Malaysia
Melaka, Ayer Keroh Melaka. His primary interests related to vision systems,
robotics and computer-integrated and image processing.
Dr Áine MacDermott is a Senior Lecturer in the School of Computer Science
and Mathematics at Liverpool John Moores University (LJMU) in the UK. She
obtained her PhD in Network Security from LJMU in 2017, and a BSc (Hons)
in Computer Forensics in 2011. Áine is a member of Research Centre for
Critical Infrastructure Computer Technology and Protection (PROTECT) at
LJMU, with research interests including the Internet of Things, collaborative
intrusion detection in interconnected networks, digital forensics, and machine learning.
USING COMBINATION OF FUZZY SET AND GRAVITATIONAL ALGORITHM FOR
IMPROVING INTRUSION DETECTION
Amin Dastanpour
1
and Raja Azlina Raja Mahmood
2
1
Computer Department, Kerman Institute of Higher Education, Kerman, Iran
2
Department of Communication Technology and Network, Faculty of Computer Science and
Information Technology, University Putra Malaysia
ABSTRACT
An intrusion detection system (IDS) is a tool used by administrators to protect networks from
unknown activities. In signature-based systems, the detection of attacks relies on predefined
patterns or behaviours associated with known threats, triggering an alert upon identification of a
match. Conversely, anomaly detection systems initiate their process by establishing a baseline
profile that reflects the normal operational behaviour of the system or network. These systems
possess the capability to identify previously unrecognized attacks, rendering them more effective
than their signature-based counterparts. Nevertheless, anomaly-based IDS must consider
numerous characteristics when pinpointing attacks Despite these difficulties, machine learning
techniques have demonstrated a strong ability to achieve highly accurate anomaly detection and
have been employed to identify attacks over the past few decades. Intrusion detection systems
are widely used methods to maintain network security. In this paper, the proposed IDS employs
machine learning approaches, namely FUZZY are initially applied, followed by optimization
algorithms such as Gravitational Search Algorithm (GSA) to determine the optimal subset of
detection features. Comparison study on the performance of the FUZZY and FUZZY-GSA
models using KDD dataset with selected optimal 27 total features, shows that the proposed
model achieves the highest detection rate with the lowest false alarm rate. The highest detection
rate for FUZZY-GSA on the KDD dataset is 98.94% in comparison to other recognition
algorithm. In summary, the proposed FUZZY-GSA model attains the highest attack recognition
percentage with the lowest false positive rate in KDD dataset.
KEYWORDS
Fuzzy, Gravitational Search Algorithm (GSA), Intrusion Detection System (IDS), Security,
Networks
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa02.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
IMPROVING INTRUSION DETECTION
Amin Dastanpour
1
and Raja Azlina Raja Mahmood
2
1
Computer Department, Kerman Institute of Higher Education, Kerman, Iran
2
Department of Communication Technology and Network, Faculty of Computer Science and
Information Technology, University Putra Malaysia
ABSTRACT
An intrusion detection system (IDS) is a tool used by administrators to protect networks from
unknown activities. In signature-based systems, the detection of attacks relies on predefined
patterns or behaviours associated with known threats, triggering an alert upon identification of a
match. Conversely, anomaly detection systems initiate their process by establishing a baseline
profile that reflects the normal operational behaviour of the system or network. These systems
possess the capability to identify previously unrecognized attacks, rendering them more effective
than their signature-based counterparts. Nevertheless, anomaly-based IDS must consider
numerous characteristics when pinpointing attacks Despite these difficulties, machine learning
techniques have demonstrated a strong ability to achieve highly accurate anomaly detection and
have been employed to identify attacks over the past few decades. Intrusion detection systems
are widely used methods to maintain network security. In this paper, the proposed IDS employs
machine learning approaches, namely FUZZY are initially applied, followed by optimization
algorithms such as Gravitational Search Algorithm (GSA) to determine the optimal subset of
detection features. Comparison study on the performance of the FUZZY and FUZZY-GSA
models using KDD dataset with selected optimal 27 total features, shows that the proposed
model achieves the highest detection rate with the lowest false alarm rate. The highest detection
rate for FUZZY-GSA on the KDD dataset is 98.94% in comparison to other recognition
algorithm. In summary, the proposed FUZZY-GSA model attains the highest attack recognition
percentage with the lowest false positive rate in KDD dataset.
KEYWORDS
Fuzzy, Gravitational Search Algorithm (GSA), Intrusion Detection System (IDS), Security,
Networks
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa02.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
REFERENCES
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intrusion detection: a comparative study," in Inventive Communication and
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[2] A. Thakkar and R. Lohiya, "A review on machine learning and deep learning
perspectives of IDS for IoT: recent updates, security issues, and challenges," Archives of
Computational Methods in Engineering, vol. 28, pp. 3211-3243, 2021.
[3] A. Thakkar and R. Lohiya, "A review of the advancement in intrusion detection datasets,"
Procedia Computer Science, vol. 167, pp. 636-645, 2020.
[4] Y. Lu, S. Chai, Y. Suo, F. Yao, and C. Zhang, "Intrusion detection for Industrial Internet
of Things based on deep learning," Neurocomputing, vol. 564, p. 126886, 2024.
[5] I. F. Kilincer, F. Ertam, and A. Sengur, "Machine learning methods for cyber security
intrusion detection: Datasets and comparative study," Computer Networks, vol. 188, p.
107840, 2021.
[6] H. Bostani and M. Sheikhan, "Hybrid of binary gravitational search algorithm and mutual
information for feature selection in intrusion detection systems," Soft computing, vol. 21,
pp. 2307-2324, 2017.
[7] L. Jin, R. Fan, X. Han, and X. Cui, "IGSA-SAC: a novel approach for intrusion detection
using improved gravitational search algorithm and soft actor-critic," Frontiers in
Computer Science, vol. 7, p. 1574211, 2025.
[8] A. Dastanpour and A. Farizani, "Improving Intrusion Detection System Using The
Combination Of Neural Network And Genetic Algorithm," Available at SSRN 5046099,
2024.
[9] H. V. Vo, H. P. Du, and H. N. Nguyen, "Apelid: Enhancing real-time intrusion detection
with augmented wgan and parallel ensemble learning," Computers & Security, vol. 136,
p. 103567, 2024.
[10] Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad,
"Network intrusion detection system: A systematic study of machine learning and deep
learning approaches," Transactions on Emerging Telecommunications Technologies, vol.
32, p. e4150, 2021.
[11] H. Ji, D. Kim, D. Shin, and D. Shin, "A study on comparison of KDD CUP 99
and NSL-KDD using artificial neural network," in Advances in Computer Science and
Ubiquitous Computing: CSA-CUTE 17, 2018, pp. 452-457.
[12] Z. Sun, G. An, Y. Yang, and Y. Liu, "Optimized machine learning enabled
intrusion detection 2 system for internet of medical things," Franklin Open, vol. 6, p.
100056, 2024.
[13] N. Mishra and S. Pandya, "Internet of things applications, security challenges,
attacks, intrusion detection, and future visions: A systematic review," IEEE Access, vol.
9, pp. 59353-59377, 2021.
[14] Y. Jiang, Z. Jia, W. Liu, L. Yang, and H. Liu, "Security identification of abnormal
access to network databases based on GSA-SVM algorithm and deep features," in Fifth
International Conference on Computer Communication and Network Security (CCNS
2024), 2024, pp. 438-443.
[15] S. Muneer, U. Farooq, A. Athar, M. Ahsan Raza, T. M. Ghazal, and S. Sakib, "A
Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A
Comprehensive Analysis," Journal of Engineering, vol. 2024, p. 3909173, 2024.
[16] F. Amiri, M. R. Yousefi, C. Lucas, A. Shakery, and N. Yazdani, "Mutual
informationbased feature selection for intrusion detection systems," Journal of network
and computer applications, vol. 34, pp. 1184-1199, 2011.
[1] G. Prethija and J. Katiravan, "Machine learning and deep learning approaches for
intrusion detection: a comparative study," in Inventive Communication and
Computational Technologies: Proceedings of ICICCT 2021, 2022, pp. 75-95.
[2] A. Thakkar and R. Lohiya, "A review on machine learning and deep learning
perspectives of IDS for IoT: recent updates, security issues, and challenges," Archives of
Computational Methods in Engineering, vol. 28, pp. 3211-3243, 2021.
[3] A. Thakkar and R. Lohiya, "A review of the advancement in intrusion detection datasets,"
Procedia Computer Science, vol. 167, pp. 636-645, 2020.
[4] Y. Lu, S. Chai, Y. Suo, F. Yao, and C. Zhang, "Intrusion detection for Industrial Internet
of Things based on deep learning," Neurocomputing, vol. 564, p. 126886, 2024.
[5] I. F. Kilincer, F. Ertam, and A. Sengur, "Machine learning methods for cyber security
intrusion detection: Datasets and comparative study," Computer Networks, vol. 188, p.
107840, 2021.
[6] H. Bostani and M. Sheikhan, "Hybrid of binary gravitational search algorithm and mutual
information for feature selection in intrusion detection systems," Soft computing, vol. 21,
pp. 2307-2324, 2017.
[7] L. Jin, R. Fan, X. Han, and X. Cui, "IGSA-SAC: a novel approach for intrusion detection
using improved gravitational search algorithm and soft actor-critic," Frontiers in
Computer Science, vol. 7, p. 1574211, 2025.
[8] A. Dastanpour and A. Farizani, "Improving Intrusion Detection System Using The
Combination Of Neural Network And Genetic Algorithm," Available at SSRN 5046099,
2024.
[9] H. V. Vo, H. P. Du, and H. N. Nguyen, "Apelid: Enhancing real-time intrusion detection
with augmented wgan and parallel ensemble learning," Computers & Security, vol. 136,
p. 103567, 2024.
[10] Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad,
"Network intrusion detection system: A systematic study of machine learning and deep
learning approaches," Transactions on Emerging Telecommunications Technologies, vol.
32, p. e4150, 2021.
[11] H. Ji, D. Kim, D. Shin, and D. Shin, "A study on comparison of KDD CUP 99
and NSL-KDD using artificial neural network," in Advances in Computer Science and
Ubiquitous Computing: CSA-CUTE 17, 2018, pp. 452-457.
[12] Z. Sun, G. An, Y. Yang, and Y. Liu, "Optimized machine learning enabled
intrusion detection 2 system for internet of medical things," Franklin Open, vol. 6, p.
100056, 2024.
[13] N. Mishra and S. Pandya, "Internet of things applications, security challenges,
attacks, intrusion detection, and future visions: A systematic review," IEEE Access, vol.
9, pp. 59353-59377, 2021.
[14] Y. Jiang, Z. Jia, W. Liu, L. Yang, and H. Liu, "Security identification of abnormal
access to network databases based on GSA-SVM algorithm and deep features," in Fifth
International Conference on Computer Communication and Network Security (CCNS
2024), 2024, pp. 438-443.
[15] S. Muneer, U. Farooq, A. Athar, M. Ahsan Raza, T. M. Ghazal, and S. Sakib, "A
Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A
Comprehensive Analysis," Journal of Engineering, vol. 2024, p. 3909173, 2024.
[16] F. Amiri, M. R. Yousefi, C. Lucas, A. Shakery, and N. Yazdani, "Mutual
informationbased feature selection for intrusion detection systems," Journal of network
and computer applications, vol. 34, pp. 1184-1199, 2011.
[17] M. Moorthy and S. Sathiyabama, "Hybrid fuzzy based intrusion detection system
for wireless local area networks," Eur. J. Sci. Res, vol. 53, pp. 431-446, 2011.
[18] S. D. Nguyen, V. S. T. Nguyen, and N. T. Pham, "Determination of the optimal
number of clusters: A fuzzy-set based method," IEEE Transactions on Fuzzy Systems,
vol. 30, pp. 3514-3526, 2021.
[19] T. Sree Kala and A. Christy, "HFFPNN classifier: a hybrid approach for intrusion
detection based OPSO and hybridization of feed forward neural network (FFNN) and
probabilistic neural network (PNN)," Multimedia Tools and Applications, vol. 80, pp.
6457-6478, 2021.
[20] K. SUPARNA and S. HALIMUNNISA, "Deep Learning Anti-Fraud Model for
Internet Loan Where We Are Going," International Journal of Information Technology
and Computer Engineering, vol. 12, pp. 269-277, 2024.
[21] A. M. Amine and Y. I. Khamlichi, "Optimization of Intrusion Detection with
Deep Learning: A Study Based on the KDD Cup 99 Database," International Journal of
Safety & Security Engineering, vol. 14, 2024.
[22] X. Zeng, "Unmasking Intruders: An In-Depth Analysis of Anomaly Detection
Using the KDD Cup 1999 Dataset," in 2024 3rd International Conference on Artificial
Intelligence and Computer Information Technology (AICIT), 2024, pp. 1-4.
[23] H. G. Kayacik, A. N. Zincir-Heywood, and M. I. Heywood, "Selecting features
for intrusion detection: A feature relevance analysis on KDD 99 intrusion detection
datasets," in Proceedings of the third annual conference on privacy, security and trust,
2005.
[24] S. Kumar, S. Gupta, and S. Arora, "A comparative simulation of normalization
methods for machine learning-based intrusion detection systems using KDD Cup’99
dataset," Journal of Intelligent & Fuzzy Systems, vol. 42, pp. 1749-1766, 2022.
[25] J. J. Tanimu, M. Hamada, P. Robert, and A. Mahendran, "Network Intrusion
Detection System Using Deep Learning Method with KDD Cup'99 Dataset," in 2022
IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-
Chip (MCSoC), 2022, pp. 251-255.
[26] A. Shehadeh, H. ALTaweel, and A. Qusef, "Analysis of Data Mining Techniques
on KDD-Cup'99, NSL-KDD and UNSW-NB15 Datasets for Intrusion Detection," in
2023 24th International Arab Conference on Information Technology (ACIT), 2023, pp.
1-6.
[27] D. Dwivedi, A. Bhushan, A. K. Singh, and Snehlata, "Improving Network
Security with Gradient Boosting from KDD Cup Dataset," SN Computer Science, vol. 5,
p. 877, 2024.
[28] M. Zhang, "Effectiveness Evaluation of Random Forest, Naive Bayes, and
Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means
Clustering," in ITM Web of Conferences, 2025, p. 04010.
[29] Y. Han, "Research on Optimization of Network Intrusion," 2025.
[30] D. D. Protić, "Review of KDD Cup ‘99, NSL-KDD and Kyoto 2006+ datasets,"
Vojnotehnički glasnik/Military Technical Courier, vol. 66, pp. 580-596, 2018.
for wireless local area networks," Eur. J. Sci. Res, vol. 53, pp. 431-446, 2011.
[18] S. D. Nguyen, V. S. T. Nguyen, and N. T. Pham, "Determination of the optimal
number of clusters: A fuzzy-set based method," IEEE Transactions on Fuzzy Systems,
vol. 30, pp. 3514-3526, 2021.
[19] T. Sree Kala and A. Christy, "HFFPNN classifier: a hybrid approach for intrusion
detection based OPSO and hybridization of feed forward neural network (FFNN) and
probabilistic neural network (PNN)," Multimedia Tools and Applications, vol. 80, pp.
6457-6478, 2021.
[20] K. SUPARNA and S. HALIMUNNISA, "Deep Learning Anti-Fraud Model for
Internet Loan Where We Are Going," International Journal of Information Technology
and Computer Engineering, vol. 12, pp. 269-277, 2024.
[21] A. M. Amine and Y. I. Khamlichi, "Optimization of Intrusion Detection with
Deep Learning: A Study Based on the KDD Cup 99 Database," International Journal of
Safety & Security Engineering, vol. 14, 2024.
[22] X. Zeng, "Unmasking Intruders: An In-Depth Analysis of Anomaly Detection
Using the KDD Cup 1999 Dataset," in 2024 3rd International Conference on Artificial
Intelligence and Computer Information Technology (AICIT), 2024, pp. 1-4.
[23] H. G. Kayacik, A. N. Zincir-Heywood, and M. I. Heywood, "Selecting features
for intrusion detection: A feature relevance analysis on KDD 99 intrusion detection
datasets," in Proceedings of the third annual conference on privacy, security and trust,
2005.
[24] S. Kumar, S. Gupta, and S. Arora, "A comparative simulation of normalization
methods for machine learning-based intrusion detection systems using KDD Cup’99
dataset," Journal of Intelligent & Fuzzy Systems, vol. 42, pp. 1749-1766, 2022.
[25] J. J. Tanimu, M. Hamada, P. Robert, and A. Mahendran, "Network Intrusion
Detection System Using Deep Learning Method with KDD Cup'99 Dataset," in 2022
IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-
Chip (MCSoC), 2022, pp. 251-255.
[26] A. Shehadeh, H. ALTaweel, and A. Qusef, "Analysis of Data Mining Techniques
on KDD-Cup'99, NSL-KDD and UNSW-NB15 Datasets for Intrusion Detection," in
2023 24th International Arab Conference on Information Technology (ACIT), 2023, pp.
1-6.
[27] D. Dwivedi, A. Bhushan, A. K. Singh, and Snehlata, "Improving Network
Security with Gradient Boosting from KDD Cup Dataset," SN Computer Science, vol. 5,
p. 877, 2024.
[28] M. Zhang, "Effectiveness Evaluation of Random Forest, Naive Bayes, and
Support Vector Machine Models for KDDCUP99 Anomaly Detection Based on K-means
Clustering," in ITM Web of Conferences, 2025, p. 04010.
[29] Y. Han, "Research on Optimization of Network Intrusion," 2025.
[30] D. D. Protić, "Review of KDD Cup ‘99, NSL-KDD and Kyoto 2006+ datasets,"
Vojnotehnički glasnik/Military Technical Courier, vol. 66, pp. 580-596, 2018.
AUTHORS
Amin Dastanpour received his B.Sc. degree in Computer Science from the
Kerman University of Iran and M.Sc. degree in Information Technology from
University Putra Malaysia. Ph.D Degree in Advance Information Technology
Special in Network Security from University Technology Malaysia. he is a
vice chancellor of Research in Kerman university, and Faculty member of
computer Science in Kerman University at Iran. his research interests include
Intrusion Detection System, network security and machine learning.
Raja Azlina received her B.Sc. degree in Computer Science from the
University of Michigan and M.Sc. degree in Software Engineering from
University Technology Malaysia. She is a faculty member of the Faculty of
Computer Science and Information Technology, University Putra Malaysia,
and is currently pursuing her Ph.D. in network security. Her research interests
include wireless network, network security and machine learning.
Amin Dastanpour received his B.Sc. degree in Computer Science from the
Kerman University of Iran and M.Sc. degree in Information Technology from
University Putra Malaysia. Ph.D Degree in Advance Information Technology
Special in Network Security from University Technology Malaysia. he is a
vice chancellor of Research in Kerman university, and Faculty member of
computer Science in Kerman University at Iran. his research interests include
Intrusion Detection System, network security and machine learning.
Raja Azlina received her B.Sc. degree in Computer Science from the
University of Michigan and M.Sc. degree in Software Engineering from
University Technology Malaysia. She is a faculty member of the Faculty of
Computer Science and Information Technology, University Putra Malaysia,
and is currently pursuing her Ph.D. in network security. Her research interests
include wireless network, network security and machine learning.
A SYSTEMATIC REVIEW OF APPLICATIONS IN FRAUD DETECTION
Hashim Jameel Shareef Jarrar
Department of Cybersecurity, College of Information Technology, Middle East University,
Amman, Jordan
ABSTRACT
The following systematic review aims to investigate the applications of data science techniques
for fraud detection (FD), especially Machine Learning (ML), Deep Learning (DL), and the
combination of both techniques in different domains, including credit card fraud and cyber
(online) fraud. The increasing sophistication of fraudulent activities necessitates advanced
detection methods, as traditional rule-based techniques often fall short. The review involves
articles from 2022 to 2024, establishing various algorithms and techniques' efficiency. Some of
the research findings show that the most frequently used FD algorithms are supervised ML
algorithms like logistic regression, decision trees, and random forests, which have high accuracy.
Also, DL techniques especially Long Short-Term Memory (LSTM) networks and convolutional
neural networks (CNNs), have been reported to provide better results, especially in real-world
problems, including e-commerce and online web-based FD. Some of the new trends that are
increasingly being incorporated to improve FD capabilities are the hybrid models that integrate
ML and DL methods. However, there are still some limitations associated with the use of ML for
FD, such as class imbalance, interpretability of the trained model, and the evolving nature of
fraud tactics. The review discusses the current trends, including real-time detection and the use
of AI in FD systems; the review also provides further research directions for overcoming the
challenges and improving the performance of FD systems. Overall, this review contributes to the
growing body of knowledge in FD and emphasizes the importance of continuous innovation in
data science applications.
KEYWORDS
Data Science; Machine Learning; Deep Learning; Fraud Detection; Cyber Fraud
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa03.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
Hashim Jameel Shareef Jarrar
Department of Cybersecurity, College of Information Technology, Middle East University,
Amman, Jordan
ABSTRACT
The following systematic review aims to investigate the applications of data science techniques
for fraud detection (FD), especially Machine Learning (ML), Deep Learning (DL), and the
combination of both techniques in different domains, including credit card fraud and cyber
(online) fraud. The increasing sophistication of fraudulent activities necessitates advanced
detection methods, as traditional rule-based techniques often fall short. The review involves
articles from 2022 to 2024, establishing various algorithms and techniques' efficiency. Some of
the research findings show that the most frequently used FD algorithms are supervised ML
algorithms like logistic regression, decision trees, and random forests, which have high accuracy.
Also, DL techniques especially Long Short-Term Memory (LSTM) networks and convolutional
neural networks (CNNs), have been reported to provide better results, especially in real-world
problems, including e-commerce and online web-based FD. Some of the new trends that are
increasingly being incorporated to improve FD capabilities are the hybrid models that integrate
ML and DL methods. However, there are still some limitations associated with the use of ML for
FD, such as class imbalance, interpretability of the trained model, and the evolving nature of
fraud tactics. The review discusses the current trends, including real-time detection and the use
of AI in FD systems; the review also provides further research directions for overcoming the
challenges and improving the performance of FD systems. Overall, this review contributes to the
growing body of knowledge in FD and emphasizes the importance of continuous innovation in
data science applications.
KEYWORDS
Data Science; Machine Learning; Deep Learning; Fraud Detection; Cyber Fraud
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa03.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
REFERENCES
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G. N., Egbedokun, G. O., & Bamisaye, T. A. (2024). An intrusion system for internet of
things security breaches using machine learning techniques. Artificial Intelligence and
Applications,
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with Help of ML Algorithms & Deep Learning Algorithms. Journal of Survey in
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[6] Airlangga, G. (2024). Evaluating the Efficacy of Machine Learning Models in Credit
Card Fraud Detection. Journal of Computer Networks, Architecture and High-
Performance Computing, 6(2), 829-837.
[7] Al-Fatlawi, A., Al-Khazaali, A. A. T., & Hasan, S. H. (2024). AI-based model for fraud
detection in bank systems. Journal of Fusion: Practice and Applications, 14(1), 19-27.
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mining techniques: A comprehensive review from 2009 to 2019. Computer Science
Review, 40, 100402.
[9] Al Balawi, S., & Aljohani, N. (2023). Credit-card fraud detection system using neural
networks. Int. Arab J. Inf. Technol., 20(2), 234-241.
[10] Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed,
M. (2022). Credit card fraud detection using state-of-the-art machine learning and deep
learning algorithms. IEEE Access, 10, 39700-39715.
[11] Alharbi, A., Alshammari, M., Okon, O. D., Alabrah, A., Rauf, H. T., Alyami, H.,
& Meraj, T. (2022). A novel text2IMG mechanism of credit card fraud detection: A deep
learning approach. Electronics, 11(5), 756.
[12] Alhashmi, A. A., Alashjaee, A. M., Darem, A. A., Alanazi, A. F., & Effghi, R.
(2023). An ensemble-based fraud detection model for financial transaction cyber threat
classification and countermeasures. Engineering, Technology & Applied Science
Research, 13(6), 12433-12439.
[13] Ali, A. A., Khedr, A. M., El-Bannany, M., & Kanakkayil, S. (2023). A powerful
predicting model for financial statement fraud based on optimized XGBoost ensemble
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[14] Aljabri, M., & Mohammad, R. M. A. (2023). Click fraud detection for online
advertising using machine learning. Egyptian Informatics Journal, 24(2), 341-350.
[15] Almazroi, A. A., & Ayub, N. (2023). Online Payment Fraud Detection Model
Using Machine Learning Techniques. IEEE Access, 11, 137188-137203.
[16] Arjunan, T. (2024). Fraud Detection in NoSQL Database Systems Using
Advanced Machine Learning. International Journal of Innovative Science and Research
Technology(IJISRT), March, 13, 248-253.
[17] Ashley Kilroy. (2024, Mar 21, 2024). Insurance Fraud Statistics 2024. Forbes.
Retrieved 28th June 2024 from https://www.forbes.com/advisor/insurance/fraud-
statistics/
[1] Abed, M., & Fernando, B. (2023). E-commerce fraud detection based on machine
learning techniques: Systematic literature review. Big Data Min. Anal.
[2] Adekunle, T. S., Alabi, O. O., Lawrence, M. O., Adeleke, T. A., Afolabi, O. S., Ebong,
G. N., Egbedokun, G. O., & Bamisaye, T. A. (2024). An intrusion system for internet of
things security breaches using machine learning techniques. Artificial Intelligence and
Applications,
[3] Adel, M., & Dubba, N. M. (2023). Detect Fraudulent Transactions Using Credit Cards
with Help of ML Algorithms & Deep Learning Algorithms. Journal of Survey in
Fisheries Sciences, 829-834.
[4] Aftab, A., Shahzad, I., Anwar, M., Sajid, A., & Anwar, N. (2023). Fraud Detection of
Credit Cards Using Supervised Machine Learning. Pak. J. Emerg. Sci. Technol. (PJEST),
4, 38-51.
[5] Ahmed, Y., Azad, M. A., & Asyhari, T. (2024). Rapid Forecasting of Cyber Events
Using Machine Learning-Enabled Features. Information, 15(1), 36.
[6] Airlangga, G. (2024). Evaluating the Efficacy of Machine Learning Models in Credit
Card Fraud Detection. Journal of Computer Networks, Architecture and High-
Performance Computing, 6(2), 829-837.
[7] Al-Fatlawi, A., Al-Khazaali, A. A. T., & Hasan, S. H. (2024). AI-based model for fraud
detection in bank systems. Journal of Fusion: Practice and Applications, 14(1), 19-27.
[8] Al-Hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data
mining techniques: A comprehensive review from 2009 to 2019. Computer Science
Review, 40, 100402.
[9] Al Balawi, S., & Aljohani, N. (2023). Credit-card fraud detection system using neural
networks. Int. Arab J. Inf. Technol., 20(2), 234-241.
[10] Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed,
M. (2022). Credit card fraud detection using state-of-the-art machine learning and deep
learning algorithms. IEEE Access, 10, 39700-39715.
[11] Alharbi, A., Alshammari, M., Okon, O. D., Alabrah, A., Rauf, H. T., Alyami, H.,
& Meraj, T. (2022). A novel text2IMG mechanism of credit card fraud detection: A deep
learning approach. Electronics, 11(5), 756.
[12] Alhashmi, A. A., Alashjaee, A. M., Darem, A. A., Alanazi, A. F., & Effghi, R.
(2023). An ensemble-based fraud detection model for financial transaction cyber threat
classification and countermeasures. Engineering, Technology & Applied Science
Research, 13(6), 12433-12439.
[13] Ali, A. A., Khedr, A. M., El-Bannany, M., & Kanakkayil, S. (2023). A powerful
predicting model for financial statement fraud based on optimized XGBoost ensemble
learning technique. Applied Sciences, 13(4), 2272.
[14] Aljabri, M., & Mohammad, R. M. A. (2023). Click fraud detection for online
advertising using machine learning. Egyptian Informatics Journal, 24(2), 341-350.
[15] Almazroi, A. A., & Ayub, N. (2023). Online Payment Fraud Detection Model
Using Machine Learning Techniques. IEEE Access, 11, 137188-137203.
[16] Arjunan, T. (2024). Fraud Detection in NoSQL Database Systems Using
Advanced Machine Learning. International Journal of Innovative Science and Research
Technology(IJISRT), March, 13, 248-253.
[17] Ashley Kilroy. (2024, Mar 21, 2024). Insurance Fraud Statistics 2024. Forbes.
Retrieved 28th June 2024 from https://www.forbes.com/advisor/insurance/fraud-
statistics/
[18] Aygul, K., Mohammadpourfard, M., Kesici, M., Kucuktezcan, F., & Genc, I.
(2024). Benchmark of machine learning algorithms on transient stability prediction in
renewable rich power grids under cyber-attacks. Internet of Things, 25, 101012.
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Financial Credit Card Fraud Detection. Journal of Economic Theory and Business
Management, 1(2), 51-57.
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phishing detection for online. computers & security, 104, 102123.
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Cost Efficiency in the Field of Automobile. Financial and Economic Review, 22(2), 77-
98.
[22] Berg, H. H., & Hansen, S. E. (2020). The stock market effect of Cybercriminals:
an empirical study of the price effects on US listed companies targeted by a data breach
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Ray, R. K., & Raihan, A. (2024). Advanced cybercrime detection: A comprehensive
study on supervised and unsupervised machine learning approaches using real-world
datasets. Journal of Computer Science and Technology Studies, 6(1), 40-48.
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detection: Opportunities, challenges, and fraud detection advancements. Future
Generation Computer Systems.
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modelling behaviour pattern using hybrid ensemble model. Arabian Journal for Science
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of Computer Science and Technology Studies, 6(1), 179-188.
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using XGBoost: an ensemble learning approach. International Journal of Information
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Detector Based on Machine Learning Techniques. Journal of Computer Science and
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Credit Card Fraud Detection. 2024 47th MIPRO ICT and Electronics Convention
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(2022). An intelligent cyber security phishing detection system using deep learning
techniques. Cluster Computing, 25(6), 3819-3828.
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security. Cyber Security and Applications, 100033.
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models and discovering important factors of health insurance fraud using machine
learning methods. Journal of Ambient Intelligence and Humanized Computing, 14(7),
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features and machine learning algorithms. International Journal of Internet Technology
and Secured Transactions, 13(2), 159-176.
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xgboost-driven machine learning and data augmentation techniques. Indatu Journal of
Management and Accounting, 1(1), 29-35.
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challenges facing insurers when detecting and preventing insurance fraud Dublin
Business School].
[69] Omer, N., Samak, A. H., Taloba, A. I., El-Aziz, A., & Rasha, M. (2024).
Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks.
Computer Systems Science & Engineering, 48(1).
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Learning Techniques for Cyberattack Prevention in IoT Systems: A Comparative
Perspective of Cybersecurity and Cyberdefense in Colombia. Electronics, 13(5), 824.
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phishing detection. Journal of King Saud University-Computer and Information Sciences,
34(2), 232-247.
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Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., & Brennan, S. E. (2021). The
PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj,
372.
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assessment techniques. International Journal of Computer Trends and Technology,
71(10), 69-79.
[74] Prasad, P. Y., Chowdary, A. S., Bavitha, C., Mounisha, E., & Reethika, C. (2023).
A comparison study of fraud detection in usage of credit cards using machine learning.
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review. Intelligent Computing Paradigm and Cutting-edge Technologies: Proceedings of
the First International Conference on Innovative Computing and Cutting-edge
Technologies (ICICCT 2019), Istanbul, Turkey, October 30-31, 2019 1,
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imbalanced class distribution using different oversampling techniques. 2022 International
Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI),
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Wu, Y., & Wang, S. (2024). A novel approach for credit card fraud transaction detection
using deep reinforcement learning scheme. PeerJ Computer Science, 10, e1998.
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(2022). Autonomous credit card fraud detection using machine learning approach☆.
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features and machine learning algorithms. International Journal of Internet Technology
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44(5), 7181-7194.
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swarm optimization algorithm for credit card fraud detection. Communications Faculty of
Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 66(1),
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AUTHOR
Dr. Hashim is an assistant professor in the Cybersecurity Dept., EMU
University - Jordan. His research interests include databases, big data,
ontologies, network security, Data Science, and image encryption.
Dr. Hashim is an assistant professor in the Cybersecurity Dept., EMU
University - Jordan. His research interests include databases, big data,
ontologies, network security, Data Science, and image encryption.
TRUST WITHOUT EXPOSURE: VERIFIABLE OBSERVABILITY WITH CAPABILITY-
NATIVE WEBASSEMBLY AT THE EDGE
Bala Subramanyan
Verifoxx, London, UK
ABSTRACT
In modern data ecosystems, where edge autonomy, privacy, and verifiability are essential,
enabling trustworthy observability without compromising data control remains a significant
challenge. This paper presents cWAMR, a capability-native WebAssembly runtime adapted for
the CHERI (Capability Hardware Enhanced RISC Instructions) architecture, enabling fine-
grained, hardware-enforced compartmentalization of untrusted code.
We demonstrate how cWAMR enables the construction of Verifiable Observability Pipelines
(VoP)—modular, staged execution flows deployed across edge environments. Each pipeline
stage is implemented as an isolated WebAssembly module running in a CHERI-sealed cWAMR
compartment, with capability-based delegation enforcing tamper-evident data flow and memory
safety without shared linear memory or enclave-based trust models.
Deployed and validated on the Arm Morello platform under the UK DSbD initiative, cWAMR
supports both interpreted and ahead-of-time WebAssembly execution, integrated with CHERI-
aware system interfaces (cWASI). The result is a lightweight, privacy-aligned foundation for
building observable, compliant edge pipelines—enabling cryptographically anchored provenance
and lifecycle assurance without cloud dependency or centralised attestation infrastructure.
KEYWORDS
WebAssembly, CHERI, Capability-Based Security, Verifiable Observability, Memory Safety,
Data Provenance, Privacy Preserving Pipelines, Data as a Product (DaaP)
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa04.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
NATIVE WEBASSEMBLY AT THE EDGE
Bala Subramanyan
Verifoxx, London, UK
ABSTRACT
In modern data ecosystems, where edge autonomy, privacy, and verifiability are essential,
enabling trustworthy observability without compromising data control remains a significant
challenge. This paper presents cWAMR, a capability-native WebAssembly runtime adapted for
the CHERI (Capability Hardware Enhanced RISC Instructions) architecture, enabling fine-
grained, hardware-enforced compartmentalization of untrusted code.
We demonstrate how cWAMR enables the construction of Verifiable Observability Pipelines
(VoP)—modular, staged execution flows deployed across edge environments. Each pipeline
stage is implemented as an isolated WebAssembly module running in a CHERI-sealed cWAMR
compartment, with capability-based delegation enforcing tamper-evident data flow and memory
safety without shared linear memory or enclave-based trust models.
Deployed and validated on the Arm Morello platform under the UK DSbD initiative, cWAMR
supports both interpreted and ahead-of-time WebAssembly execution, integrated with CHERI-
aware system interfaces (cWASI). The result is a lightweight, privacy-aligned foundation for
building observable, compliant edge pipelines—enabling cryptographically anchored provenance
and lifecycle assurance without cloud dependency or centralised attestation infrastructure.
KEYWORDS
WebAssembly, CHERI, Capability-Based Security, Verifiable Observability, Memory Safety,
Data Provenance, Privacy Preserving Pipelines, Data as a Product (DaaP)
For More Details : https://aircconline.com/ijnsa/V17N4/17425ijnsa04.pdf
Volume Link : https://airccse.org/journal/jnsa25_current.html
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AUTHOR
Bala Subramanyan is a technologist and researcher with over 14 years
of experience in secure systems architecture, privacy-preserving
computation, and applied cryptography. He is the Co-Founder and CTO
of Verifoxx, where he is the principal architect of a universal privacy
infrastructure layer that leverages advanced PETs—including zero-
knowledge proofs, verifiable computation, and trusted execution—to
enable verifiable insights without exposing raw data. His work spans confidential computing,
WebAssembly-based secure runtimes, and functional encryption. Bala’s research and applied
innovations have been featured in IEEE conferences and cryptographic forums such as IACR.
Prior to co-founding Verifoxx, he led technical & R&D initiatives at JP Morgan, Lockheed
Martin, Nationwide, and IHS, contributing to the design of scalable, proof-based systems for
secure computation across finance, healthcare, and identity domains.
Bala Subramanyan is a technologist and researcher with over 14 years
of experience in secure systems architecture, privacy-preserving
computation, and applied cryptography. He is the Co-Founder and CTO
of Verifoxx, where he is the principal architect of a universal privacy
infrastructure layer that leverages advanced PETs—including zero-
knowledge proofs, verifiable computation, and trusted execution—to
enable verifiable insights without exposing raw data. His work spans confidential computing,
WebAssembly-based secure runtimes, and functional encryption. Bala’s research and applied
innovations have been featured in IEEE conferences and cryptographic forums such as IACR.
Prior to co-founding Verifoxx, he led technical & R&D initiatives at JP Morgan, Lockheed
Martin, Nationwide, and IHS, contributing to the design of scalable, proof-based systems for
secure computation across finance, healthcare, and identity domains.
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