SMART SENTRY CYBER THREAT INTELLIGENCE IN IIOT

SMART SENTRY CYBER THREAT INTELLIGENCE IN IIOT

1. SMART SENTRY CYBER THREAT INTELLIGENCE IN IOT Members of batch: M Lakshmi Rama Chandra Mouli A Naga Tanmai R Sri Sowmya Shaik Majeed Mohammad Akbar Name of the Guide: Mr S Girish Chandra Designation: Assistant Professor

2. Table Of Contents Statement of the problem Abstract Introduction Literature Survey Software & Hardware Requirements Advantages Base/Reference papers

3. Statement of the Problem The increasing interconnectivity of Industrial Internet of Things ( IIoT ) systems has significantly enhanced operational efficiency but also introduced vulnerabilities to cyber threats that jeopardize critical infrastructure security. Traditional security measures are often inadequate in addressing the complexities and unique challenges posed by IIoT environments, where the scale of data and the speed of operations demand more advanced solutions.

4. Abstract Cyber threats that are aimed at Industrial Internet of Things ( IIoT ) systems are a threat to the security of critical infrastructure. SmartSentry is a comprehensive Cyber Threat Intelligence (CTI) framework for IIoT environments which I have developed. This framework incorporates the use of sophisticated machine learning algorithms and deep learning models for the identification and management of cyber threats. Some of the key algorithms are Random Forest (RF), Decision Tree (DT), Extra Tree Classifier (ETC), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and Deep Neural Network (DNN).Some of the techniques like Synthetic Minority Over-sampling Technique (SMOTE) helps in making the model more resilient to data imbalance which is very crucial in IIoT anomaly detection

5. Introduction

6. Literature Survey S.NO YEAR AUTHORS TITLE OUT COMES 1 2022 M. A. Ferrag, O. Friha, D.Hamouda, L. Maglaras, & H. Janicke EdgeIIoTset : A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning, The Edge- IIoTset dataset, created for IoT / IIoT applications, includes diverse device data and analyzes fourteen attack types, facilitating machine learning-based intrusion detection in centralized and federated learning modes. 2 2022 K. Gupta, D.K.Sharma, K. Datta Gupta, and A. Kumar, A tree classifier based network intrusion detection model for Internet of medical things The proposed tree classifier-based model enhances IoMT network security by efficiently reducing input data dimensions for faster anomaly detection, achieving high accuracy of 94.23% while ensuring patient privacy and safety.

7. S.NO YEAR AUTHORS TITLE OUT COMES 3 2022 G.P.Bhandari , A.Lyth , A.Shalaginov , and T.-M. Grønli , Artificial intelligence enabled middleware for distributed cyberattacks detection in IoT -based smart environments This article proposes an AI-based middleware for detecting cyberattacks in smart environments, utilizing a four-step process that includes data aggregation, AI model application, deployment, and performance evaluation. 4 2023 G. Bhandari, A.Lyth , A.Shalaginov , and T.-M. Grønli ‘Distributed deep neural-network-based middleware for cyber-attacks detection in smart IoT ecosystem: A novel framework and performance evaluation approach, This study presents an AI-based framework for detecting malware in IoT devices, achieving 93% detection accuracy and minimal resource consumption, enhancing security in Smart Environments against cyberattacks. 5 2017 M. M. Rashid, S.U.Khan , F.Eusufzai , M. A. Redwan , S. R. Sabuj , and M. Elsharief , A federated learning-based approach for improving intrusion detection in industrial Internet of Things networks The paper proposes a Federated Learning method for intrusion detection in IoT networks, enhancing privacy by training local device data while achieving accuracy (92.49%) similar to centralized ML models (93.92%).

8. Software & Hardware Requirements

9. Advantages

10. Base/Reference Papers

11. THANK YOU!!