Abstract: The increasing sophistication of cyber threats targeting Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) networks necessitates an advanced threat detection framework. This field focuses on developing a Quantum-Adaptive Threat Detection (QATD) model to enhance cybersecurity resilience, improve detection accuracy, and minimize false positives. Utilizing a dataset comprising real-world ICS/SCADA threat incidents, the system implements quantum-inspired anomaly detection techniques and graph-based threat correlation to identify malicious activities in real time. The QATD model is benchmarked against conventional detection systems, including signature-based Intrusion Detection Systems (IDS), anomaly-based AI models, and machine learning classifiers, using performance metrics such as Detection Accuracy, False Positive Rate (FPR), Precision, and Response Time Efficiency. The system integrates Quantum Graph-Based Threat Correlation (QGTC) and Quantum-Optimized Attack Response (QOAR) mechanisms, significantly improving attack pattern recognition and automated mitigation strategies. The proposed system achieves over 90% accuracy in zero-day attack detection, reduces false positives by 40%, and enhances response efficiency by 50% compared to traditional AI-based cybersecurity solutions.
Keywords: ICS Security, SCADA Threat Detection, Quantum-Adaptive Threat Detection (QATD), Cybersecurity Analytics, AI-Driven Threat Mitigation, Zero-Day Attack Detection.
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DOI:
10.17148/IJARCCE.2025.14539
