Quantum-Inspired Feature Learning and Parameter-Efficient Neural Architectures for IoT Intrusion Detection: A Comprehensive Review and Future Research Roadmap

Abstract

Rapid growth in the Internet of Things (IoT) networks means that almost limitless connectivity of intelligent devices and advanced technologies in the domains of healthcare, transport, industrial processes, and smart city automation are possible. However, the mass rollout of IoT devices with limited resources has exposed IoT networks to numerous cyber threat vectors including botnets, distributed denial-of-service (DDoS) attacks, the spread of malware, and unauthorized intrusions to computer networks. This has prompted the evolution of Intrusion Detection Systems (IDSs) from traditional machine learning (ML) techniques to various advanced deep learning techniques used for the detection and analysis of sophisticated cyberattack techniques on network traffic. Within this context, deep neural networks, convolutional neural networks, recurrent neural networks, and various autoencoder models, have all produced significant results with respect to attack detection; however, practical implementations have been constrained by a considerable amount of time and space resources. This has led to a focused interest in quantum-inspired learning approaches, which improve feature distinguishability for normal and attack traffic through the application of high dimensional probabilistic mappings. In addition, frameworks that utilize parameter-efficient design based on tensor decomposition, low-rank, and Kronecker factorizations, have reduced the time and space resources needed for attack detection. A comprehensive overview of IoT intrusion detection research, including benchmark datasets and ML and deep learning models, as well as quantum-inspired and structured neural approaches is provided in this paper. Additionally, current approaches are evaluated on their strengths and weaknesses, and major research gaps are noted. Lastly, the focus is on the development of the next generation of IoT security that is intelligent, scalable, and able to provide real-time protection.