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 exploit this powerful tool by presenting the latest innovations and applications of GANs in cybersecurity.
