Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Solanki, Shashank; Sinha, Rituraj Title A name given to the resource Synergizing Edge AI and Quantum Machine Learning for Real-Time Cyber Threat Mitigation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Advancing Cyber Threat Detection Through Quantum and Edge Computing;pp.163-188 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-3551-3.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-3551-3.ch006</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019125840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019125840?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Solanki S., Christ University, India; Sinha R., Christ University, India Description An account of the resource 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. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833733553-7; 979-833733551-3; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online