A State of The Art: AI-Enabled Intrusion Detection Systems

Abstract

As digital networks expand at an unprecedented rate, alongside with the progression of cyber-attacks which present significant challenges to traditional security measures. Although intrusion detection systems (IDSs) have long served as the primary line of defense, they often struggle to keep pace with novel ,zero-day, and unknown threats. Recent advancements in Artificial Intelligence— particularly through ML and and DL approaches—By learning from evolving attack patterns, AI-powered IDS can dynamically adapt, offering faster and more precise threat detection. This paper provides a state of the art review of AI-enabled IDS approaches by examining architectures, detection techniques, and performance metrics across a range of benchmark datasets.Our comparative analysis highlights the superior accuracy of deep learning approaches on modern datasets, while also examining the impact of dataset quality, detection of rare attacks, and model efficiency.It also analysis demonstrates that while AI-driven methods markedly enhance detection accuracy and reduce false alarm rates, persistent challenges remain, especially in reliably classifying rare and novel attack types due to imbalanced datasets and computational constraints.This study offers valuable insights for future advancements toward robust, real-world intrusion detection systems