Innovative AI-Driven Intelligent Attack Detection, Prediction, and Autonomous Response for Next-Generation Cyber Security
TL;DR
This chapter explores innovative AI technologies, including Machine Learning, Deep Learning, Reinforcement Learning, Explainable AI, and Generative AI, for intelligent attack detection, prediction, and mitigation and discusses current challenges, implementation limitations, and future research directions.
Abstract
The rapid evolution of intelligent cyber-attacks has challenged traditional cybersecurity mechanisms, necessitating the adoption of Artificial Intelligence (AI)-driven defense strategies. Advanced threats such as ransomware, zero-day exploits, Advanced Persistent Threats (APTs), and AI-powered phishing campaigns require adaptive and autonomous security solutions capable of real-time detection and response. This chapter explores innovative AI technologies, including Machine Learning, Deep Learning, Reinforcement Learning, Explainable AI, and Generative AI, for intelligent attack detection, prediction, and mitigation. A unified AI-driven cybersecurity framework is proposed that integrates threat intelligence, behavioural analytics, anomaly detection, explainable decision-making and autonomous incident response to enhance cyber resilience. A case study demonstrates the practical implementation of the framework in an enterprise environment. The chapter also discusses current challenges, implementation limitations, and future research directions, providing researchers and practitioners with insights into developing scalable, trustworthy, and next-generation AI-enabled cybersecurity systems.