Next-Generation Cyber Intrusion Defense using Quantum-Inspired Machine Learning across Enterprise Cloud Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2026.0904009Keywords:
quantum-inspired machine learning, cybersecurity, intrusion detection, enterprise cloud, anomaly detectio n, machine learning, cyber defense, cloud security, quantum-inspired optimization, cyber resilienceAbstract
The increasing dependence of enterprises on cloud platforms has created complex cybersecurity environments characterized by distributed workloads, dynamic resource allocation, interconnected services, and rapidly changing attack surfaces. Conventional intrusion detection approaches can struggle to identify sophisticated and previously unseen attacks because they frequently depend on static signatures, manually engineered features, or computationally intensive analysis. Quantum-inspired machine learning (QIML) provides an emerging computational approach that combines concepts derived from quantum computing with classical machine learning algorithms. Although it does not necessarily require quantum hardware, QIML can exploit quantum-inspired optimization, probabilistic representations, and advanced search mechanisms to improve the analysis of large and complex cybersecurity datasets. This study examines the potential of QIML for next-generation cyber intrusion defense across enterprise cloud platforms. The research focuses on developing an adaptive intrusion detection framework capable of processing heterogeneous cloud telemetry, identifying anomalous behavior, classifying attack patterns, and supporting resilient security operations. The proposed methodology integrates cloud network flows, authentication events, application logs, endpoint telemetry, API activity, and infrastructure events. Classical machine learning models are compared with quantum-inspired approaches using measures including accuracy, precision, recall, F1-score, false-positive rate, detection latency, computational cost, scalability, and robustness. The study further evaluates model performance under concept drift, class imbalance, novel attacks, and adversarial conditions. The research argues that quantum-inspired optimization can complement conventional machine learning and provide a promising pathway toward adaptive, scalable, and resilient intrusion detection for increasingly complex enterprise cloud environments
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