Artificial Intelligence for Intrusion Detection Systems
1st Edition
Published on 26. May 2025
Book
Paperback/Softback
204 pages
978-1-032-38696-6 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Postgraduate and Undergraduate Advanced
Illustrations
47 s/w Tabellen, 51 s/w Abbildungen, 51 s/w Photographien bzw. Rasterbilder
47 Tables, black and white; 51 Halftones, black and white; 51 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 12 mm
Weight
342 gr
ISBN-13
978-1-032-38696-6 (9781032386966)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Mayank Swarnkar | Shyam Singh Rajput
Artificial Intelligence for Intrusion Detection Systems
E-Book
10/2023
1st Edition
Chapman & Hall/CRC
€69.99
Available for download
Mayank Swarnkar | Shyam Singh Rajput
Artificial Intelligence for Intrusion Detection Systems
E-Book
10/2023
1st Edition
Chapman & Hall/CRC
€69.99
Available for download
Mayank Swarnkar | Shyam Singh Rajput
Artificial Intelligence for Intrusion Detection Systems
Book
10/2023
1st Edition
Chapman & Hall/CRC
€145.50
Shipment within 10-20 days
Persons
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.
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.
Content
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.