"This book is aligned with the cyber security issues and provides a wide view of the novel cyber-attacks and the defence mechanisms, especially AI-based IDS. This book serves as a reference book for scientific investigators who need to analyse Intrusion Detection Systems (IDS), as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate-level course in information security"--
This book is aligned with the cyber security issues and provides a wide view of the novel cyber-attacks and the defence mechanisms, especially AI-based IDS.
This book is associated with the cybersecurity issues and provides a wide view of the novel cyber
attacks and the defense mechanisms, especially AI-based Intrusion Detection Systems (IDS).
Features:
• A systematic overview of the state-of-the-art IDS
• Proper explanation of novel cyber attacks which are much different from classical cyber attacks
• Proper and in-depth discussion of AI in the field of cybersecurity
• Introduction to design and architecture of novel AI-based IDS with a trans- parent view of
real-time implementations
• Covers a wide variety of AI-based cyber defense mechanisms, especially in the field of
network-based attacks, IoT-based attacks, multimedia attacks, and blockchain attacks.
This book serves as a reference book for scientific investigators who need to analyze IDS, as well
as researchers developing methodologies in this field. It may also be
used as a textbook for a graduate-level course on information security.
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.
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.
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