Artificial Intelligence for Intrusion Detection Systems

Häftad, Engelska, 2025

Av Mayank Swarnkar, Shyam Singh Rajput, India) Swarnkar, Mayank (IIT BHU, India) Rajput, Shyam Singh (NIT Patna

849 kr

Beställningsvara. Skickas inom 7-10 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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 IDSProper explanation of novel cyber attacks which are much different from classical cyber attacksProper and in-depth discussion of AI in the field of cybersecurityIntroduction to design and architecture of novel AI-based IDS with a trans- parent view of real-time implementationsCovers 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.

Produktinformation

Utgivningsdatum2025-05-26
Mått156 x 234 x 12 mm
Vikt400 g
FormatHäftad
SpråkEngelska
Antal sidor204
FörlagTaylor & Francis Ltd
ISBN9781032386966

Tillhör följande kategorier

Informationsteknik: allmänt inom Data och it
Systemvetenskap och AI inom Data och it
Energiteknik inom Naturvetenskap och teknik
Hårdvara inom Data och it
Elektronik och kommunikationer inom Naturvetenskap och teknik
Artificiell intelligens inom Data och it
IT-säkerhet inom Data och it

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.