Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review

Abstract

The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote monitoring, and control, and introduces substantial security challenges, especially in defending these devices against cyber threats. Intrusion Detection Systems (IDS) are crucial for securing IoT; however, traditional IDS often struggle to adapt to IoT networks' dynamic and evolving nature and threat patterns. A potential solution is using Deep Reinforcement Learning (DRL) to enhance IDS adaptability, enabling them to learn from and react to their operational environment dynamically. This systematic review examines the application of DRL to enhance IDS in IoT settings, covering research from the past ten years. This review underscores the state-of-the-art DRL techniques employed to improve adaptive threat detection and real-time security across IoT domains by analyzing various studies. Our findings demonstrate that DRL significantly enhances IDS capabilities by enabling systems to learn and adapt from their operational environment. This adaptability allows IDS to improve threat detection accuracy and minimize false positives, making it more effective in identifying genuine threats while reducing unnecessary alerts. Additionally, this systematic review identifies critical research gaps and future research directions, emphasizing the necessity for more diverse datasets, enhanced reproducibility, and improved integration with emerging IoT technologies. This review aims to foster the development of dynamic and adaptive IDS solutions essential for protecting IoT networks against sophisticated cyber threats.