Rademics Research Institute

Abstract

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.

Introduction

As digital transformation accelerates, organizations are increasingly exposed to sophisticated and evolving cybersecurity threats [1]. The rapid growth in internet-connected devices, cloud infrastructure, and the expansion of digital services have expanded the attack surface, making traditional security measures insufficient [2-4]. Cybersecurity systems today must contend with various malicious activities, including data breaches, Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), and insider threats [5-7]. These attacks are more complex and difficult to detect with conventional methods that rely on fixed signatures or static rules [8]. Consequently, there was a pressing need for adaptive, scalable, and intelligent security solutions that can address these challenges in real-time [9,10].

Traditional cybersecurity strategies largely depend on rule-based detection, predefined heuristics, and signatures to recognize threats [11]. While effective against known attacks, these methods are often ineffective when faced with novel or zero-day threats [12]. The inability to anticipate or quickly adapt to new attack vectors leaves organizations vulnerable [13,14]. Classical machine learning (ML) models, while offering some degree of automation and pattern recognition, are often overwhelmed by the high-dimensionality of data in modern networks and can struggle to identify more complex or hidden attack patterns [15,16]. These models typically require extensive training datasets and are limited in their ability to generalize to unseen data, which results in false positives and negatives [17]. These limitations highlight the need for more advanced approaches to detect and mitigate cyber risks, especially in high-speed, data-rich environments like cloud computing and large enterprise networks [18-20].