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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Usha Banavikal Ajay
BMS Institute of Technology and Management
Computer Science & IT Engineering

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A reinforcement learning-based intrusion detection system for internet of things with zero-day resilience Sowmya Somanath; Usha Banavikal Ajay; Sangeetha Kodlipet Nanjundaswamy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4691-4699

Abstract

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.
Co-Authors Sangeetha Kodlipet Nanjundaswamy Sowmya Somanath