Quantum Inspired Neural Networks for Next Generation Cybersecurity Threat Prediction and Response
Sreejith Sreekandan Nair · 2025
The integration of Quantum-Inspired Neural Networks (QINNs) into cybersecurity frameworks represents a transformative approach for addressing modern cyber threats. As traditional methods struggle to manage the complexity and high-dimensionality of data in dynamic environments, QINNs offer advanced capabilities for threat detection, response, and adaptation. By leveraging quantum-inspired algorithms, these neural networks enhance the detection of sophisticated attacks, such as advanced persistent threats (APTs), zero-day vulnerabilities, and insider threats. This chapter explores the core principles of QINNs, highlighting their potential in cloud security, intrusion detection systems (IDS), and real-time cybersecurity analytics. It examines the comparative advantages of QINNs over classical machine learning models, showcasing their superior adaptability, optimization, and processing efficiency. The chapter also discusses hybrid quantum-classical approaches, emphasizing their applicability in enhancing the resilience of cybersecurity systems. This work provides a comprehensive analysis of QINNs and their future impact on next-generation cybersecurity strategies.