Adaptive Cyber Defense: Leveraging Neuromorphic Computing for Advanced Threat Detection and Response

Abstract

As the complexity of the digital landscape evolves, so does the sophistication of cyber threats, necessitating advanced cybersecurity measures. Despite significant strides in threat detection and response using machine learning and deep learning techniques, these systems grapple with high false positive rates, limited adaptability to evolving threats, and computational inefficiency in real-time data processing. This study proposes to delve into the potential of Neuromorphic Computing (NC) to address these challenges. Inspired by the human brain's principles, NC offers rapid, efficient information processing through Spiking Neural Networks (SNNs) and other brain-inspired architectures. The study hypothesizes that integrating NC into cyber defence could enhance threat detection, response times, and adaptability, thereby bolstering cybersecurity systems' resilience. However, the implementation of NC in cybersecurity is fraught with challenges, including scalability, compatibility with existing infrastructures, and the creation of secure, robust neuromorphic systems. This study elucidates these challenges, proposes potential solutions, and highlights future research directions in this promising field. With focused research and development, NC could revolutionize cybersecurity, enhancing the defence mechanisms of the digital ecosystems against the relentless onslaught of cyber threats. The study analyses that the incorporation of NC into cybersecurity is not only feasible but also necessary in increasingly digital world.