Implementation of a Decentralized Intrusion Detection System for IoT Networks
Bindia Venugopal, Antony Jaya Mabel Rani · 2025
As the Internet grows, the Internet of Things (IoT) has become very big and is one of the biggest security issues while deploying an attack of sophisticated cyber qualities. There is a centralized approach to the existing Intrusion Detection Systems (IDS) in the sense that the adaptation is limited and has not handled dynamic and scale IoT networks. Hence, Distributed AI-Driven Threat Intelligence Model (DAITIM), based deep neural network intrusion detector for efficient mechanism detection in IoT networks is suggested distributed threat intelligence model. The model uses the logs from edge devices in a decentralized manner and recovers the malicious patterns of traffic known as zero day attack. The result of this work shows that DAITIM has achieved scalability and resilience improvement in increasing the security of IoT.