(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
The escalating sophistication and frequency of cyber threats necessitate the evolution of Intrusion Detection Systems (IDS) to effectively safeguard digital infrastructures. Traditional IDS approaches often fall short in detecting novel or zero-day attacks due to their reliance on predefined signatures and rules. In response, Artificial Intelligence (AI) has emerged as a transformative force in enhancing IDS capabilities. AI-powered IDS leverage machine learning (ML) and deep learning (DL) techniques to analyze vast amounts of network traffic data, identifying patterns and anomalies indicative of potential intrusions. Recent advancements in AI have led to the development of systems capable of adaptive learning, enabling them to detect previously unseen threats. For instance, Generative Adversarial Networks (GANs) have been employed to generate synthetic attack data, augmenting training datasets and improving detection accuracy for rare attack scenarios . Additionally, Reinforcement Learning (RL) has been utilized to dynamically optimize firewall configurations, enhancing real-time threat mitigation . The integration of Explainable AI (XAI) into IDS frameworks has further improved system transparency, allowing security analysts to understand and trust AI-driven decisions . Moreover, the application of AI in Industrial Cyber-Physical Systems (ICPS) has demonstrated the feasibility of deploying intelligent IDS in complex and critical environments . This paper reviews the state-of-the-art AI-powered IDS developed in 2024, highlighting their architectures, methodologies, and performance metrics. It also discusses the challenges and future directions in the field, emphasizing the need for continuous adaptation to counter emerging cyber threats effectively.