Synergizing Edge AI and Quantum Machine Learning for Real-Time Cyber Threat Mitigation

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Author(s): Shashank Solanki (Christ University, India)and Rituraj Sinha (Christ University, India)
Copyright: 2026
Pages: 26
Source title: Advancing Cyber Threat Detection Through Quantum and Edge Computing
Source Author(s)/Editor(s): Shenson Joseph (University of North Dakota, USA), Kishor Kumar Reddy C. (Stanley College of Engineering and Technology for Women, India), Asegul Hulus (Association for Computing Machinery, Cyprus)and Tatjana Sibalija (Union University, Serbia)
DOI: 10.4018/979-8-3373-3551-3.ch006

Keywords: Cyber and Network Security / Information Science Reference / Knowledge Management and Library Science / Quantum Information & Computing

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Abstract

The escalation of the complexity of cyber threats must be countered by traditional signature- and rule-based security approaches. In this study, we propose a hybrid Edge AI–Quantum Machine Learning (QML) framework that employs variational quantum circuits and classical neural networks towards real-time per–device threat detection. Using three case studies, we validate the framework: (1) fraud detection in high frequency trading with 17% more true positives and 22% less false positives; (2) inference times under 100 ms for IoT anomaly detection; and (3) reduction of over 25% in deepfake misclassification. The built system is built end-to-end with an open-source stack. Finally, regulatory and ethical considerations (GDPR, data, privacy, international cybersecurity protocols, etc., Budapest Convention) are discussed. In presenting this work, we present a scalable and adaptive model for next-generation cybersecurity.

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Synergizing Edge AI and Quantum Machine Learning for Real-Time Cyber Threat Mitigation

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