Advanced AI for Network Security
Faisal Asad Farid Aburub, Saad Alateef · Advances in computational intelligence and robotics book series · 2025
Organizations continue facing threats that exploit network vulnerabilities. Defenders seek approaches that detect anomalies and respond quickly to attacks. This chapter examines predictive models and automated mechanisms for network defense. It explores how artificial intelligence approaches learn evolving patterns in large-scale data, offering insights and mitigation strategies. Researchers observe advantages in automated recognition, anomaly forecasts, and coordinated countermeasures. Studies indicate success in reducing detection delays and halting malicious actions. Defense frameworks featuring reinforcement agents, neural architectures, and adaptive analytics show promise in lessening manual involvement. Outcomes suggest that ongoing refinements in data handling, architecture design, and interpretability are essential. This chapter provides a synthesis of methods, findings, and future research directions for next-generation AI-driven network defense.