Adaptive and Quantum-Resilient Intrusion Detection for Wireless Sensor Networks and IoT Environments
Received: 5 February 2025 | Revised: 26 February 2025 and 12 March 2025 | Accepted: 17 March 2025 | Online: 9 June 2025
Corresponding author: Mathan Kumar Mounagurusamy
Abstract
Integrating Wireless Sensor Networks (WSNs) with the Internet of Things (IoT) has transformative potential for data acquisition, processing, and decision-making across dynamic connected environments. Ensuring the security and integrity of these systems is paramount, especially in the face of increasingly sophisticated cyber threats. This study introduces a novel security framework that combines Quantum Key Distribution (QKD) with an adaptive Deep Reinforcement Learning (DRL)-based Intrusion Detection System (IDS), specifically designed to address the unique challenges of the WSN-IoT ecosystem. The key innovation lies in integrating QKD not only for encryption but also as a dynamic quantum-secure layer that continuously adapts to security requirements based on real-time threats and communication patterns. Unlike previous approaches that focus primarily on routing and resource allocation, the proposed framework employs DRL with Proximal Policy Optimization (PPO) to refine intrusion detection by adapting its policies based on evolving attack signatures and threat types. This dual-layer QKD-DRL approach enhances intrusion detection accuracy and establishes a self-optimizing, quantum-secure communication protocol. Tested using the CICIDS2017 dataset, the proposed model achieved a 99.75% detection rate, outperforming traditional Random Forest (97.12%) and Deep Neural Network (96.88%) models. This improvement underscores the efficacy of combining quantum cryptographic techniques with DRL-based adaptive learning, providing a robust, real-time defense mechanism for IoT-driven environments in applications such as smart cities, healthcare, and industrial IoT systems. Thus, the proposed QKD-DRL framework sets a new standard for scalable, secure communication and threat mitigation in the IoT ecosystem.
Keywords:
quantum key distribution, deep reinforcement learning, wireless sensor networks, Internet of Things, intrusion detection, cyber security, deep Q-networkDownloads
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Copyright (c) 2025 Mathan Kumar Mounagurusamy, A. Anil Kumar Reddy, C. M. Velu, Gera Vijaya Nirmala, D. Arivazhagan, Myasar Mundher Adnan, K. Rahmaan, T. Prabhakaran
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