The proliferation of interconnected devices in the Internet of Things and wireless sensor networks creates ever-expanding opportunities for cyberattacks, yet traditional security systems struggle to keep pace within these resource-limited environments. Hamid Barati from Islamic Azad University and colleagues present a new approach to intrusion detection, combining the power of evolutionary algorithms with self-supervised learning to address these challenges. Their system employs a quantum genetic algorithm to efficiently select the most important features and optimise performance, while simultaneously learning from unlabeled data to reduce the need for extensive, manually created training sets. This innovative framework demonstrates improved accuracy, lower false alarm rates, and greater computational efficiency compared to existing methods, suggesting a promising path towards more robust and scalable security solutions for the growing world of connected devices.
The system addresses growing security challenges in these environments by combining the strengths of quantum-inspired optimization and self-supervised learning techniques, improving detection accuracy and reducing reliance on labeled datasets. Experiments demonstrate that the QGA-SSL IDS achieves superior performance compared to existing methods, with the combination of QGA for feature selection and SSL for representation learning leading to better accuracy and efficiency. The authors highlight the unique security vulnerabilities of IoT/WSN environments, including resource constraints, dynamic topologies, and increasing network traffic. The proposed IDS is designed to be adaptable, scalable, and efficient, with QGA identifying relevant features for intrusion detection and SSL learning meaningful representations from unlabeled data, enhancing the system’s ability to detect novel attacks. This research positions the QGA-SSL IDS as an advancement over traditional and recent machine learning-based IDSs.
Quantum Genetic Algorithm Enhances Intrusion Detection
The increasing prevalence of Internet of Things (IoT) and Wireless Sensor Networks (WSN) creates expanding vulnerabilities to cyber threats, prompting the development of more effective intrusion detection systems. Traditional systems often struggle in resource-constrained environments due to high computational demands and reliance on extensive labeled datasets. To address these limitations, scientists have proposed a novel hybrid Intrusion Detection System that integrates a Quantum Genetic Algorithm (QGA) with Self-Supervised Learning (SSL), offering a promising solution for securing these networks. The team’s innovative approach leverages the principles of quantum computing, specifically the QGA, to optimize feature selection and fine-tune model parameters, ensuring efficient detection even on devices with limited processing power.
Simultaneously, SSL enables the system to learn robust representations directly from unlabeled data, significantly reducing the need for manually labeled training sets. This combination allows the system to adapt to evolving threats and maintain high performance. Experiments conducted on benchmark IoT intrusion datasets demonstrate the superior performance of this new system compared to conventional evolutionary and deep learning-based IDS models, achieving higher detection accuracy while minimizing false positive rates and computational cost.
Quantum Optimised Intrusion Detection for Sensor Networks
This research presents a novel hybrid Intrusion Detection System (IDS) designed for resource-constrained wireless sensor networks and Internet of Things environments. This system combines self-supervised learning with a Quantum Genetic Algorithm to effectively identify network intrusions while minimizing reliance on large, labeled datasets. The self-supervised learning component extracts meaningful features from unlabeled data, and the Quantum Genetic Algorithm optimizes both feature selection and model configuration, resulting in a system that balances accuracy with computational efficiency. Evaluations on standard datasets demonstrate that the proposed system outperforms existing intrusion detection models in terms of accuracy, F1-score, and false positive rate. Importantly, tests on Raspberry Pi-based wireless sensor nodes confirm the system’s practicality and lightweight nature, making it suitable for real-world IoT deployments. The key contributions of this work include the introduction of self-supervised learning for IDS in these environments, the development of a quantum-inspired optimization strategy, and the demonstration of a hybrid system that effectively balances performance and feasibility.
👉 More information
🗞 A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things
🧠ArXiv: https://arxiv.org/abs/2509.03744