A reinforcement learning-based intrusion detection system for internet of things with zero-day resilience

Sowmya Somanath (BMS Institute of Technology and Management)
Usha Banavikal Ajay (BMS Institute of Technology and Management)
Sangeetha Kodlipet Nanjundaswamy (JSS Academy of Technical Education)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 5: October 2026

The conventional intrusion detection systems (IDS) are often found to be ineffective when faced with zero-day attacks that are novel compared to previously learned behavior patterns. This paper presents the development of a robust and adaptive IDS for the internet of things (IoT) using a deep Q-network (DQN) agent. A custom environment is designed in which state abstraction is applied to convert high-dimensional feature vectors into discrete state spaces. The proposed agent model is trained to detect normal and malicious traffic using a reward and penalty mechanism to encourage the agent to take correct decisions. The validation of the proposed methodology is carried out using the UNSW-NB15 dataset under both known attack and zero-day attack scenarios. The results showed detection accuracies of 99.56% for known attacks and 99.11% for zero-day attacks.