Neuromorphic Security Models for Cross-Domain Distributed Systems in Adversarial Environments

While the world is shifting towards the distributed systems over diverse domains, their security in adversarial environments has become quite challenging. The conventional se- curity models usually fail to handle such a complex and dynamic nature of multi-domain networks. In this paper, neuromorphic security models are presented for enhancing the resilience of cross-domain distributed systems against sophisticated attacks by taking inspiration from architectural aspects of the human brain. We develop and train neuromorphic algorithms with real- world data sets to detect and mitigate threats in real time. Our approach focused on adaptability and learning; the system has to evolve with the developments in security threats. We show through extensive experimentation that the neuromorphic models outperform traditional security mechanisms both in accuracy and response time, especially in highly adversarial environments. These results prove that neuromorphic computing might provide a game-changing role for security strategies of distributed systems. Hence, they provide a robust framework resistant to modern cyber threats’ complexities. This work opens up perspectives toward further development in the field of brain- inspired security solutions for secure and resilient distributed infrastructures.