Hybrid Quantum-Kernel and Quantum-Inspired Machine Learning for TDoS Early Warning in Critical Infrastructure

Abstract

: Increasing digitalization exposes critical infrastructure to sophisticated cyber threats, requiring new approaches to improving security and resilience. While classical machine learning techniques have shown promise in anomaly detection and threat mitigation, emerging quantum-inspired methods offer new opportunities to enhance detection capabilities by leveraging principles derived from quantum computing. The objective of this work is to propose a model for the early detection of Telephony Denial of Service attacks using a combination of classical algorithms and quantum computing-based techniques. Call records are embedded into a low-dimensional quantum feature space using spatial and temporal attributes, mapped through a quantum kernel in two modalities: Quantum-Inspired Kernel or Qiskit-Simulated Quantum Kernel. This approach enables the identification of subtle and complex patterns that can be difficult to capture using classical methods alone, without the need for fully fault-tolerant quantum hardware. The anomaly decision threshold is empirically calibrated, allowing the detector to balance early sensitivity against false-alarm control under the evaluated Public Safety Answering Points (PSAP) traffic conditions. The evaluation shows that the proposed framework can improve threat detection and system resilience in critical infrastructure contexts. The results support the use of kernel-based quantum-inspired representations as a tunable early-warning layer for PSAP traffic monitoring, while also showing that threshold calibration and operational context remain necessary before deployment.