AI-Powered Cyber Risk Management System Using IoT and BiLSTM-Based Threat Intelligence

In today's hyper-connected digital landscape, organizations are confronting an escalating tide ofincreasingly complex and rapidly evolving cyber threats. This challenge is profoundly exacerbated across thevast and distributed systems enabled by the Internet of Things (IoT), where the sheer volume of devices andtheir constant communication create an expansive and often vulnerable attack surface. Traditionalcybersecurity solutions, typically reliant on static, signature-based detection methods, inherently struggle toadapt and respond in real time to novel or polymorphic threats, leaving critical IoT infrastructure and sensitivedata highly susceptible to exploitation.This project introduces the comprehensive design and development of an innovative AI-powered automatedtool, meticulously engineered for seamless integration with heterogeneous IoT devices, specifically to addressthese emerging and dynamic cyber risks more effectively. By leveraging the continuous, real-time streams ofoperational and behavioral data generated from diverse IoT sensors and network endpoints, the system appliesadvanced deep learning—specifically Bidirectional Long Short-Term Memory (BiLSTM) networks. Thesenetworks are uniquely capable of analyzing intricate temporal sequences and learning complex behavioralbaselines, allowing them to precisely detect subtle anomalies and assess potential vulnerabilities acrossinterconnected networks. Unlike conventional static detection methods, BiLSTM models possess theintelligence to understand contextual patterns over time, identifying nuanced changes in device behavior ornetwork traffic that could signify a nascent cyberattack or a compromised system.