Quantum Machine Learning Assisted Zero-Day Threat Detection for Next-Generation Cloud Security Architectures
Keywords:
Quantum Machine Learning, Zero-Day Threat Detection, Cloud Security, Quantum Computing, Cybersecurity, Anomaly Detection, Zero Trust, Hybrid Quantum-Classical Computing, Threat Intelligence, Cloud-Native Security, Variational Quantum Classifier, Quantum Support Vector MachineAbstract
The rapid adoption of cloud computing, multi-cloud infrastructures, containerized applications, serverless services, and distributed workloads has significantly expanded the cyberattack surface of modern digital enterprises. Conventional security mechanisms primarily depend on signatures, predefined rules, and historically observed attack patterns, making them less effective against zero-day threats that exploit previously unknown vulnerabilities. Quantum Machine Learning (QML) introduces an emerging computational paradigm that combines quantum computing principles with machine learning techniques to improve the analysis of complex and high-dimensional cybersecurity data. This research proposes a Quantum Machine Learning Assisted Zero-Day Threat Detection framework for next-generation cloud security architectures. The proposed framework integrates cloud telemetry, network-flow information, identity behavior, application logs, vulnerability intelligence, and workload-level security events into a unified detection pipeline. Classical preprocessing and feature-engineering mechanisms prepare security data before selected features are transformed into quantum representations and processed through quantum-enhanced learning models such as Variational Quantum Classifiers and Quantum Support Vector Machines. The framework incorporates anomaly detection, behavioral profiling, risk scoring, and adaptive response mechanisms to identify previously unseen malicious activities. A hybrid quantum-classical architecture is emphasized because current quantum hardware remains limited in scalability, noise tolerance, and availability. The proposed approach aims to improve detection sensitivity, reduce false positives, accelerate complex pattern analysis, and support adaptive cloud security operations. The research provides a conceptual foundation for integrating QML with zero-trust, cloud-native, and autonomous cybersecurity architectures.
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