Artificial Intelligence for Intrusion Detection Systems

Dr. Mayank Swarnkar is currently working as an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (Banaras Hindu University) Varanasi. He completed his Ph.D. from the Indian Institute of Technology Indore in 2019. He completed his M.Tech in Wireless Communication and Computing from the Indian Institute of Information Technology Allahabad in 2013 and B.E. in IT from Government Engineering College Jabalpur in 2011. He also worked as Software Engineer at NEC Technologies India for 1 year from 2013 to 2014 and as Assistant Professor at Bennett University for 1 year from 2019 to 2020. His primary areas of interest are Network and System Security. He works mainly in the field of Network Traffic Classification, Zero Day Attacks, Intrusion Detection Systems, and VoIP Spam Detection. He has given many invited talks and he is a reviewer of many reputed conferences and journals. Dr. Shyam Singh Rajput received a B.E. degree in Computer Science & Engineering (CSE) from R. G. P.V., Bhopal, Madhya Pradesh, India, in 2011. He earned his M.Tech degree and Ph.D. degree in CSE from ABV-Indian Institute of Information Technology & Management (ABVIIITM), Gwalior, India, in 2013 and 2019, respec- tively. He has more than seven years of experience teaching undergraduate and post- graduate classes. Presently, he is working as an assistant professor in the Department of CSE, National Institute of Technology Patna, India. His current research interests include image processing, computer vision, and wireless networks. He has published more than 40 journal articles, conference papers, and book chapters in the domain of image processing, biometrics, wireless ad hoc networks, and information security. He has published three Indian patents and edited books with Elsevier and CRC Press. He is a member of IEEE and ACM.

• 1. Intrusion detection system using artificial intelligence. 2. Robust, Efficient and Interpretable Adversarial AI Models for Intrusion Detection in Virtualization Environment. 3. Detection of Malicious Activities by Smart Signature-based IDS. 4. Detection of Malicious Activities by AI-supported Anomaly-based IDS. 5. An Artificial Intelligent Enabled Framework for Malware Detection. 6. IDS for Internet of Things (IoT) and Industrial IoT Network. 7. An Improved NIDS using RF based feature selection technique and voting classifier. 8. Enhanced AI-based Intrusion Detection and Response System for WSN. 9. Methodology for Programming of AI-based IDS.