Generative Adversarial Networks for Cybersecurity:
Protecting Data and Networks
1st Edition
Published on 6. May 2026
Book
Hardback
248 pages
978-1-041-09801-0 (ISBN)
Description
Generative Adversarial Networks (GANs) play a crucial dual role in cybersecurity, serving both as powerful defensive tools and sophisticated attack vectors that security professionals must understand and counter. GANs are invaluable for generating synthetic datasets to train cybersecurity models when real attack data is scarce or sensitive, creating realistic network traffic patterns for testing intrusion detection systems, and augmenting threat intelligence by simulating various attack scenarios without exposing actual vulnerabilities.
Exploring the application of GAN models in intrusion detection, anomaly detection, and cybercrime, Generative Adversarial Networks for Cybersecurity: Protecting Data and Networks covers how GANs can be applied to pinpoint security holes, vulnerabilities, viruses, malware, phishing attacks, and other security risks. It explains how advanced GANs integrated with such digital technologies as the Internet of Things (IoT), cloud-native computing, edge analytics, serverless technology, and blockchain to protect and secure data and information from security breaches. The book also discusses how GANs can identify outliers, performance bottlenecks, and other issues in cloud infrastructure modules, applications, and data. Other topics featured in the book include:
GAN-based security's ethical and privacy concerns
GANs and explainable artificial intelligence (AI)
Building trustworthy sixth-generation (6G) networks with Generative Adversarial Learning (GAL)
Intrusion detection systems enhanced by GANs
GANs are a valuable tool for enhancing cybersecurity efforts by generating synthetic data and images that can show unusual patterns in data. This book helps researchers, academics, and professionals realize the potential of this powerful tool by presenting the latest innovations and applications of GANs in cybersecurity.
Exploring the application of GAN models in intrusion detection, anomaly detection, and cybercrime, Generative Adversarial Networks for Cybersecurity: Protecting Data and Networks covers how GANs can be applied to pinpoint security holes, vulnerabilities, viruses, malware, phishing attacks, and other security risks. It explains how advanced GANs integrated with such digital technologies as the Internet of Things (IoT), cloud-native computing, edge analytics, serverless technology, and blockchain to protect and secure data and information from security breaches. The book also discusses how GANs can identify outliers, performance bottlenecks, and other issues in cloud infrastructure modules, applications, and data. Other topics featured in the book include:
GAN-based security's ethical and privacy concerns
GANs and explainable artificial intelligence (AI)
Building trustworthy sixth-generation (6G) networks with Generative Adversarial Learning (GAL)
Intrusion detection systems enhanced by GANs
GANs are a valuable tool for enhancing cybersecurity efforts by generating synthetic data and images that can show unusual patterns in data. This book helps researchers, academics, and professionals realize the potential of this powerful tool by presenting the latest innovations and applications of GANs in cybersecurity.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic and Postgraduate
Illustrations
38 s/w Zeichnungen, 6 s/w Tabellen, 38 s/w Abbildungen
6 Tables, black and white; 38 Line drawings, black and white; 38 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
640 gr
ISBN-13
978-1-041-09801-0 (9781041098010)
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
E. Chandra Blessie | Pethuru Raj | B. Sundaravadivazhagan
Generative Adversarial Networks for Cybersecurity:
Protecting Data and Networks
E-Book
05/2026
Auerbach
€125.99
Available for download
E. Chandra Blessie | Pethuru Raj | B. Sundaravadivazhagan
Generative Adversarial Networks for Cybersecurity:
Protecting Data and Networks
E-Book
05/2026
Auerbach
€125.99
Available for download
Persons
Dr. E. Chandra Blessie is currently working as dean, Innovation, School of Innovation, KG College of Arts and Science, Coimbatore, Tamil Nadu, India. She has 23 years of teaching experience. She is guiding Ph.D. research scholars. She has published 4 books. She has published two patents, 22 book chapters, 46 studies in reputed Journals indexed by Scopus, Springer, the Institute of Electrical and Electronics Engineers (IEEE), and Elsevier, and 36 studies in international conference proceedings. She has published a study on Cybersecurity. Her areas of research interest are deep learning, big data analytics, graph representation learning, knowledge graphs, and security. She has been the reviewer of Springer Conferences and Journals. She has organized many Faculty Development Programs (FDPs), workshops, guest lectures, and webinars. She has acted as a resource person in Workshops/ FDPs/ Symposiums. She has been a resource person for 6 days in the All India Council for Technical Education and the Indian Society for Technical Education (AICTE-ISTE) Refresher Course/Orientation Programme. She has delivered guest lectures at various colleges and has acted as chief guest, reviewer, and chairperson at national and international conferences. She has done International Business Machines (IBM) certifications in Data Analytics, certification on "Deep Learning," Data Science, and Structured Query Language (SQL) Server Administration. She was honored as "Best Active Participation-Woman Member" by the Computer Society of India. She is the chief editor of the book, "Recent Advancements in Engineering and Management".She is member of the Computer Society of India (CSI), Institute of Advanced Scientific Research, and the International Association of Computer Science and Information Technology (IACSIT). She is a member of the Board of Studies in Dr. N.G. P. Arts and Science, Coimbatore, Tamil Nadu, India. She is a member of the PPG Institute of Technology Advisory Board. She was the chairperson of the Computer Society of India, Coimbatore Chapter, Tamil Nadu, India, during the 2022-2023 academic year.
Dr. Pethuru Raj works at Principal AI Architect, Infocion Inc., Karnataka, India. Previously, He worked at Reliance Jio Platforms Ltd. (JPL) in Bengaluru, Karnataka, India, IBM Global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has gained over 22 years of IT industry experience and 8 years of research experience. Dr. Raj completed a CSIR-sponsored Ph.D. degree at Anna University, Chennai, Tamil Nadu, India and continued with the University Grants Commission (UGC)-sponsored postdoctoral research in the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bengaluru, Karnataka, India. He was granted two international research fellowships, Japan Society for the Promotion of Science (JSPS) and Japan Science and Technology (JST) Agency to work as a research scientist for 3.5 years in two leading Japanese universities. He has been a professional member of Association for Computing Machinery (ACM) and IEEE. He focuses on some of the digital transformation technologies such as the Internet of Things (IoT), artificial intelligence (AI), atreaming data analytics, blockchain,digital twins, cloud-native computing, edge and serverless computing, reliability engineering, microservices architecture (MSA), event-driven architecture (EDA) and 5G/6G.
Dr. B. Sundaravadivazhagan is an experienced researcher and educator in Information and Communication Engineering. He has more than 21 years of teaching and research experience and earned his Ph.D. in Information and Communication Engineering from Anna University in Chennai in 2016. He is a member of various professional bodies such as IEEE, Information Systems Audit and Control Association (ISACA), Indian Society for Technical Education (ISTE), and ACM, and has published over 40 research articles in Science Citation Index (SCI) and Scopusindexed journals. He has also served as a resource person, keynote speaker, and advisory committee member in more than 20 international and national conferences. He has received two research grants from the Ministry of Higher Education, Research and Innovation (MoHERI) and The Research Council (TRC), Oman. His research interests include IoT, AI and machine learning, deep learning, cloud computing, networks and cybersecurity, wireless networks, and mobile ad-hoc Networks (MANETs). He has published articles in cybersecurity. He is a reputed journal editor and reviewer and serves on the Doctoral Committee as an international committee member for Amrita University, Bengaluru, Karnataka, India, as well as an adjunct faculty member at the Saveetha School of Engineering, Chennai, Tamil Nadu, India.
Dr. Pethuru Raj works at Principal AI Architect, Infocion Inc., Karnataka, India. Previously, He worked at Reliance Jio Platforms Ltd. (JPL) in Bengaluru, Karnataka, India, IBM Global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has gained over 22 years of IT industry experience and 8 years of research experience. Dr. Raj completed a CSIR-sponsored Ph.D. degree at Anna University, Chennai, Tamil Nadu, India and continued with the University Grants Commission (UGC)-sponsored postdoctoral research in the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bengaluru, Karnataka, India. He was granted two international research fellowships, Japan Society for the Promotion of Science (JSPS) and Japan Science and Technology (JST) Agency to work as a research scientist for 3.5 years in two leading Japanese universities. He has been a professional member of Association for Computing Machinery (ACM) and IEEE. He focuses on some of the digital transformation technologies such as the Internet of Things (IoT), artificial intelligence (AI), atreaming data analytics, blockchain,digital twins, cloud-native computing, edge and serverless computing, reliability engineering, microservices architecture (MSA), event-driven architecture (EDA) and 5G/6G.
Dr. B. Sundaravadivazhagan is an experienced researcher and educator in Information and Communication Engineering. He has more than 21 years of teaching and research experience and earned his Ph.D. in Information and Communication Engineering from Anna University in Chennai in 2016. He is a member of various professional bodies such as IEEE, Information Systems Audit and Control Association (ISACA), Indian Society for Technical Education (ISTE), and ACM, and has published over 40 research articles in Science Citation Index (SCI) and Scopusindexed journals. He has also served as a resource person, keynote speaker, and advisory committee member in more than 20 international and national conferences. He has received two research grants from the Ministry of Higher Education, Research and Innovation (MoHERI) and The Research Council (TRC), Oman. His research interests include IoT, AI and machine learning, deep learning, cloud computing, networks and cybersecurity, wireless networks, and mobile ad-hoc Networks (MANETs). He has published articles in cybersecurity. He is a reputed journal editor and reviewer and serves on the Doctoral Committee as an international committee member for Amrita University, Bengaluru, Karnataka, India, as well as an adjunct faculty member at the Saveetha School of Engineering, Chennai, Tamil Nadu, India.
Content
1. Generative Adversarial Networks (GANs) in Cybersecurity: Exploring Opportunities and Challenges 2. A Study on Generative Adversarial Networks (GANs) for Cybersecurity: Variants and Challenges 3. Leveraging Generative Adversarial Networks for Enhanced Cybersecurity 4. Building Trustworthy 6G Networks with Generative Adversarial Learning 5. Optimizing Techniques for Data Generation Using Generative Adversarial Network 6. Advancing Anomaly Detection via GANs: A Comprehensive Review and Experimental Analysis 7. A Study on Generative Adversarial Networks Insights in Industry 5.0 8. Securing Cyberspace: A GAN-Driven Approach to Phishing Website Detection 9. GAN in AI Security: Enforcing Integrity in Innovation 10. Cloud Security: A Comprehensive Analysis of Intrusion Detection Systems (IDS) Enhanced by Generative Adversarial Networks (GANs) 11. Graph Neural Network Approach for Intelligent Bot Detection, Enhancing CAPTCHA Security 12. Advancing Cybersecurity with Generative Adversarial Networks and Explainable AI: A Comprehensive Exploration 13. Securing Blockchain: A Paradigm Shift with Generative Adversarial Networks 14. Enhancing Cyberthreat Intelligence Feeds Using Generative Adversarial Networks 15. Cyber Security Augmentation Using GAN-Enhanced Image Processing 16. GAN-Based Metaheuristic Techniques for Accurate Data Generation and Imbalance Data Control 17. Ethical and Privacy Considerations in GAN-Based Security