Advances in artificial intelligence are reshaping the cyber‑security lan‎dscape. Generative AI techniques an‎d self‑adaptive learning methods offer new ways of detecting an‎d responding to sophisticated cyber threats. This report presents a comprehensive examination of the integration of generative models with adaptive defensive learning techniques for enhancing intrusion detection systems (IDSs). We provide a detailed problem statement illustrating how static rule‑based an‎d signature‑driven IDSs fail against polymorphic an‎d zero‑day attacks. We review related work, covering classical machine learning (ML), deep learning (DL), generative models, reinforcement learning (RL), data normalization, feature engineering, an‎d real‑world datasets. Building on this foundation, we propose a methodological framework that combines data augmentation through generative models, unsupervised anomaly detection, supervised classification, an‎d adaptive RL agents for real‑time defense. We analyze how reinforcement learning can enable self‑adaptive policies that learn optimal detection thresholds an‎d mitigation actions in dynamic environments. We discuss practical considerations, including scalability, privacy, regulatory compliance, ethical implications, an‎d implementation challenges. Case studies drawn from existing literature illustrate the benefits an‎d limitations of our approach. Finally, we identify open research directions an‎d present conclusions.

2/14/2026 12:00:00 AM

90128

1405/02/06

Study of Enhancing Cyber Threat Detection Systems using Generative AI Models an‎d Self‑Adaptive Defensive Learning Techniques

يادگيري عميق , يادگيري تقويتي , اينترنت اشيا , سيستم‌هاي تشخيص نفوذ

deep learning , reinforcement learning , Internet‑of‑Things , intrusion detection systems