Interpretable Deep learning Framework for early detection of Cyber-attacks and Malwares in Internet of Things (IoT)

This project develops an Intrusion Detection System (IDS) that provide a critical security measure to helps protecting Internet of Things (IoT) devices and networks from cyber threats. IoT devices are particularly vulnerable to attacks due to their limited processing power, memory, and energy resources, as well as their reliance on wireless communication protocols. IDS can help detect and prevent unauthorized access, attacks, and data breaches by monitoring network traffic and individual devices for signs of compromise. Our goal is to effectively secure IoT devices, and networks by developing an interpretable distributed learning system that can be easily trusted by the stackholders and security specialists. The outcome of our project is an AI tool that can help organizations to effectively detect and respond to security threats in real-time, protecting their IoT devices and networks from cyberattacks.