Synergizing Edge AI and Quantum Machine Learning for Real-Time Cyber Threat Mitigation
- Title
- Synergizing Edge AI and Quantum Machine Learning for Real-Time Cyber Threat Mitigation
- Creator
- Solanki, Shashank; Sinha, Rituraj
- Description
- 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. 2026, IGI Global Scientific Publishing. All rights reserved.
- Source
- Advancing Cyber Threat Detection Through Quantum and Edge Computing;pp.163-188
- Date
- 01-01-2025
- Publisher
- IGI Global
- Coverage
- Solanki S., Christ University, India; Sinha R., Christ University, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833733553-7; 979-833733551-3;
- Format
- online
- Language
- English
- Type
- Book chapter
Collection
Citation
Solanki, Shashank; Sinha, Rituraj, “Synergizing Edge AI and Quantum Machine Learning for Real-Time Cyber Threat Mitigation,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/24749.