Intelligent Intrusion Detection Systems – A comprehensive overview of applicable AI Methods with a Focus on IoT Security

The rapid advancement of technology and the increasing complexity of cyber threats have necessitated the development of more sophisticated security measures. This paper presents a structured analysis of how artificial intelligence (AI) methods enhance the accuracy, adaptability, and efficiency of Intrusion Detection Systems (IDS). Different AI approaches, including machine learning, deep learning, and reinforcement learning are categorized and evaluated, highlighting their practical applications and limitations. The main focus is on enhancing the detection capabilities of IDS. By examining supervised, unsupervised, and reinforcement learning approaches, the study highlights how these methods can improve the accuracy, efficiency, and adaptability of IDS in identifying both known and novel threats. Additionally, the paper addresses the challenges associated with AI-based IDS, such as the need for extensive datasets, computational demands, and vulnerability to adversarial attacks. The findings underscore the transformative impact of AI on IDS and suggest directions for future research to further advance the field. With the exponential growth of Internet of Things (IoT) devices, securing networked environments has become increasingly challenging due to their resource constraints, diverse communication protocols, and exposure to cyber threats. Lightweight IDS models may provide solutions for the computational overhead, the scalability and privacy issues. This overview aims to serve as a valuable resource for researchers and practitioners seeking to leverage AI to bolster cybersecurity defenses. This paper not only provides a historical perspective but also critically analyzes current advancements and future research directions with a particular focus on IoT security and lightweight intrusion detection models.