Quantum-Enhanced Intrusion Detection Systems for IoT Networks: Trends, Challenges, and Future Directions

Abstract: This survey examines quantum-enhanced machine learning techniques for intrusion detection in Internet of Things (IoT) and fog environments, synthesizing recent work on hybrid quantum-classical models, quantum convolutional neural networks (QCNNs), quantum support vector machines (QSVMs), and quantum-assisted clustering. We review five recent primary studies that apply quantum methods to IoT security, summarize their methodological choices (encoding, circuit ansatz, classical-quantum interfaces), and evaluate reported benefits and limitations with respect to accuracy, computational cost, and deployability in resource-constrained settings. The literature indicates potential gains in feature expressivity and false-negative reduction from quantum encodings and variational circuits, but practical barriers remain: limited qubit counts, noisy hardware, encoding overhead, and scarce real-world deployment studies. We identify concrete research gaps and propose directions for lightweight encodings, noise-aware hybrid architectures, federated quantum-assisted Intrusion Detection System (IDS), and standardized benchmarks for reproducible evaluation. The survey aims to guide researchers and practitioners toward scalable, explainable, and deployable quantum-enhanced intrusion detection for IoT.

Keywords: Internet of Things, Intrusion Detection System, Quantum Machine Learning, Quantum Convolutional Neural Network, Quantum Support Vector Machine, Quantum K-Means, Hybrid Quantum Neural Network.