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 AI
- Building trustworthy 6G networks with Generative Adversarial Learning
- 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.
Table of Contents:
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
About the Author :
Dr. E. Chandra Blessie is the Dean of Innovation, School of Innovation, KG College of Arts and Science, Coimbatore, India.
Dr. Pethuru Raj works at Reliance Jio Platforms Ltd. (JPL) in Bangalore, India. Previously. He worked in IBM Global Cloud Center of Excellence (CoE), Wipro consulting services (WCS), and Robert Bosch Corporate Research (CR).
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, India.
