Leveraging Machine Learning to Enhance Cybersecurity v2.pptx

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as a vital component in improving cybersecurity measures, providing increased capabilities for more efficient threat detection, analysis, and response than older methods. Its incorporation into cybersecurity takes advantage of the ability to process large amounts of data quickly, discovering patterns and anomalies that could suggest a security problem.

1. Bahaa Farouk Chief Transformation Officer, Suez Canal Bank Leveraging AI to Enhance Cybersecurity 1

2. Acknowledgement 2 In advance, appreciating the judge board of speakers/topics selection especially Professor Dr. Bahaa Hassan, Chairman and Founder Arab Security Conference it is an honor to participate and deliver such professional experience session in its 8 th edition, 2024. Further, it is an honor to be here for the third time in row among cybersecurity experts across the globe.

3. Agenda 3 AI in Cybersecurity, in nutshell Benefits of Applying AI in Cybersecurity AI Uses Cases in Cybersecurity Detailed Use Cases Deep Learning in Threat Detection ML in SDLC Secure Code Scanning Future Readiness, Recommendations References ddd

4. 4 Scan to participate in opening survey.

5. Survey Analysis, 22 nd Sep. 2024 5

6. 6 The Impact of Artificial Intelligent in Improving Cybersecurity AI in Cybersecurity, in nutshell?

7. AI in Cybersecurity, in nutshell 7 Cyber threats evolve in complexity and frequency, Traditional cybersecurity measures struggle to keep pace, Artificial Intelligent offers a paradigm shift enabling proactive threat detection and adaptive response strategies. Debate! There are some concerns of replying on AI in Cybersecurity.

8. AI in Cybersecurity, in nutshell 8 Debate! Three major concerns: Bias in decision-making Lack of Explanatory & Transparency Potential of Misuse/Abuse Source: Deloitte Research https://www2.deloitte.com/us/ en /insights/focus/cognitive-technologies/ai-investment-by- country.html

9. 9 The Impact of Artificial Intelligent in Improving Cybersecurity Benefits?

10. Benefits of Adapting AI in Cybersecurity 10

11. 11 The Impact of Artificial Intelligent in Improving Cybersecurity AI Use Cases in Cybersecurity

12. AI Use Cases In Cybersecurity 12 Artificial intelligence has brought lots of positive effects on cybersecurity. AI can detect and stop threats in real-time without interfering with the business processes, and AI can track data that escapes human eyes including chats, emails, video and other modes of communications.

13. AI Use Cases In Cybersecurity Threat Detection 13 Machine learning models analyze network traffic patterns to identify anomalies that may indicate cyber threats. Supervised learning techniques utilize labeled datasets to recognize known threats, and Unsupervised learning detects novel threats by identifying deviations from normal behavior.

14. AI Use Cases In Cybersecurity Fraud Prevention 14 By analyzing transaction patterns , these models can flag anomalies indicative of fraud, Reducing false positives and, enhancing accuracy . Common fraud types that can be detected: Card Fraud , Fake Account Creation, Account Takeover ATO, and Credential Stuffing

15. AI Use Cases In Cybersecurity 15 Automate incident response. Hence, reducing damage and speed up recovery. Automate processes like quarantining compromised devices/files or reverting modifications done by an attacker.

16. AI Use Cases In Cybersecurity 16 Natural Language Processing (NLP) techniques are used to analyze email content and detect phishing attempts. ML models learn from vast datasets of phishing emails to identify subtle cues that humans might miss .

17. AI Use Cases In Cybersecurity 17 AI-powered behavioral analysis can help reduce the risk of security breaches and strengthen an organization’s overall security posture. Indicators of Attack (IOA) are proactive, compared to Indicators of Compromise (IOC). Behavioral Analytics has several types: User & Entity Behavioral Analytics UBEA , Network Behavioral Analytics NBA Insider Threat Behavioral Analytics ITBA

18. 18 The Impact of Artificial Intelligent in Improving Cybersecurity Detailed Use Cases: Deep Learning in Threat Detection

19. Deep Learning in Threat Detection 19 A revolution in network technology has been ushered in by Software Defined Networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security Deep learning (DL) and machine learning (ML) have been implemented in SDN-based Network Intrusion Detection System (NIDS) to overcome the security issues within a network. Deep learning, a subset of ML, excels in processing Unstructured data, such as images and text. Both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are particularly effective in identifying complex patterns within cybersecurity datasets.

20. Deep Learning in Threat Detection 20 Data Plane All network devices are immersed of collector agents Control Plane Records of network flow are collected by the data collector Application Plane The constructed and implemented model of ML is used as an application of SDN Source: Academic Paper 2022 https://www.mdpi.com/1424-8220/22/20/7896

21. 21 The Impact of Artificial Intelligent in Improving Cybersecurity Detailed Use Cases: ML in SDLC Secure Code Scanning

22. ML in SDLC Secure Code Scanning 22 Integrating ML into the SDLC enhances secure code scanning by identifying vulnerabilities early in the development process . ML models assess code quality and flag potential security issues , enabling developers to address them promptly. ML models can suggest fixes to identified issues .

23. ML in SDLC Secure Code Scanning 23 No only detecting the source code vulnerability, but also GenAI would suggest a fix! Source SAST Tool Documentation https:// github.blog /ai-and-ml/ llms /how-ai-enhances-static-application-security-testing- sast /

24. 24 The Impact of Artificial Intelligent in Improving Cybersecurity Future Readiness?

25. Adapting AI in Cybersecurity Concerns 25 Debate! Three major concerns: Bias in decision-making Lack of Explanatory & Transparency Potential of Misuse/Abuse Source: Deloitte Research https://www2.deloitte.com/us/ en /insights/focus/cognitive-technologies/ai-investment-by- country.html

26. Future Readiness? 26 Invest in Training:  Equip security teams with the necessary skills to develop and maintain ML models. Prioritize Data Security:  Ensure data used for training is secure and representative of actual threat landscapes. Foster Collaboration : Encourage collaboration between data scientists and security experts to enhance model development. Adopt a Proactive Approach :  Use ML to anticipate and mitigate potential threats before they manifest. Continuously Evaluate Models:  Regularly review and update ML models to maintain their effectiveness against evolving threats .

27. References 27 Academic Research Papers/Books Ahmed N, Ngadi Ab, Sharif JM, Hussain S, Uddin M, Rathore MS, Iqbal J, Abdelhaq M, Alsaqour R, Ullah SS, et al. Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction. Sensors. 2022; 22(20):7896 Alghamdi A and Barsoum (2024). A Comprehensive IDs to Detect Botnet Attacks Using Machine Learning Techniques2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI)10.1109/ICMI60790.2024.10585846(1-6) Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials. Chio, C., & Freeman, D.. Machine Learning and Security: Protecting Systems with Data and Algorithms. O'Reilly Media. Lippmann, R. P., et al. Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. DARPA Information Survivability Conference and Exposition. Saxe, J., & Berlin, K.. Deep neural network-based malware detection using two-dimensional binary program features. 10th International Conference on Malicious and Unwanted Software. Shah, S. A., & Issac, B.. Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Generation Computer Systems. Others https://www2.deloitte.com/us/ en /insights/focus/cognitive-technologies/ai-investment-by- country.html https:// github.blog /ai-and-ml/ llms /how-ai-enhances-static-application-security-testing- sast / ddd

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