Quantum-AIO ChameleonGAN

Cybersecurity is entering a critical phase where adversaries exploit camouflage, mutation, and zero-day techniques to evade detection. This book introduces Quantum-AIO ChameleonGAN, a multidisciplinary defense architecture combining quantum computing, chameleon-inspired adaptation, and angle of incidence optimization (AIO) to counter modern threats. Unlike conventional intrusion detection systems, the framework synthesizes polymorphic adversarial patterns, dynamically adjusts detection sensitivity, and quantifies deviations through angular metrics for greater interpretability. Extensive experimental validation shows that Quantum-AIO ChameleonGAN significantly outperforms classical GAN-based systems, variational autoencoders, and ensemble detectors in both accuracy and robustness. The model also supports lightweight deployment on edge and IoT environments, bridging the gap between high-performance research and real-world application. A forward-looking layer, Temporal Evolution Tracking (TET), predicts adversarial trajectories-positioning cybersecurity as anticipatory rather than reactive.

Autorentext
Edward Fondo is a researcher and lecturer in Computer Science and Information Technology at the Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya. His work focuses on artificial intelligence, quantum computing, and cybersecurity.