Anomaly Detection Using Quantum Neural Networks: A Quantum-Driven Approach to Cyber Threat Identification

View Sample PDF

Author(s): Laya Billinty Varra (Stanley College of Engineering and Technology for Women, India), Jagadeshwari Puttanapura (University of South Alabama, USA), C. Kishor Kumar Reddy (Stanley College of Engineering and Technology for Women, India), Shikha Khullar (Poornima University, India), Ghita Lazrek (Lab LIASSE, ENSA, Université Sidi Mohamed Ben Abdellah, Fez, Morocco)and Jothi Paranthaman (Botho University, Botswana)
Copyright: 2026
Pages: 28
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.ch008

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

Purchase

View Anomaly Detection Using Quantum Neural Networks: A Quantum-Driven Approach to Cyber Threat Identification on the publisher's website for pricing and purchasing information.

Abstract

Quantum Neural Networks (QNNs) offer a new paradigm that takes advantage of quantum computing principles to emulate and improve on the functional benefits offered by classical neural networks. This chapter offers a broad investigation on QNNs in the context of anomaly detection in important real-world settings such as cybersecurity, fraud detection in financial transactions, real-time medical diagnostics, and many more. This chapter will provide a general overview of QNNs, their theoretical background, advantages with respect to classical models, and application or implementation success in anomaly detection for high-dimensional, noisy and streaming data. The chapter will also cover architectures, training methodologies, and simulation platforms for QNNs, and also include case studies to demonstrate these concepts. Finally, it will address their limitations, challenges in practical implementations, and future research possibilities.

The IRMA Community

Research IRM

Anomaly Detection Using Quantum Neural Networks: A Quantum-Driven Approach to Cyber Threat Identification

Purchase

Abstract

Related Content

IRMA Sponsors