A Dynamic Reward-Based Deep Reinforcement Learning for IoT Intrusion Detection
Kezhou Ren, Lijun Liu, Hongtao Bai, Yuan Wen · 2024
The rapid proliferation of Internet of Things (IoT) technology has enhanced human quality of life while simultaneously introducing significant cybersecurity challenges. IoT devices are constrained by limited computational resources, storage capacity, and power supply, rendering them susceptible to botnet exploitation and Distributed Denial of Service (DDoS) attacks. Conventional signature-based intrusion detection systems (IDS) are frequently deployed to mitigate network attacks in IoT environments. However, these systems heavily rely on manual expert knowledge and exhibit limited adaptability to emerging threats, particularly when confronted with novel attacks such as zero-day exploits. Deep reinforcement learning (DRL), which enables agents to make autonomous decisions through policy function approximation, has demonstrated promising results in network attack identification. This paper proposes a DRL-based intrusion detection system for identifying diverse attack vectors in IoT environments. To enhance the model's sensitivity to multi-class samples, we design a dynamic reward function, thereby improving the overall network attack recognition capabilities. The proposed model is validated using the Bot-IoT dataset. Experimental results indicate that the model achieves 99% accuracy in classification.