Blockchain-Based Iot Intrusion Detection System With Multiagent Systems.

Abstract

With the increasing adoption of IoT technologies, securing IoT networks has become increasingly critical. Traditional intrusion detection systems face challenges in IoT environments due to resource constraints and the complexity of these networks. This tpes of research develops, evaluates, and implements an intrusion detection system incorporating deep learning algorithms, blockchain technology, and a hybrid placement approach within a multi-agent framework. The system comprises modules for response, analysis, data management, and data collection. Testing is conducted using the NSL-KDD dataset from the National Security Lab-Knowledge Discovery and Data Mining. The findings indicate that deep learning approaches are viable for intrusion detection in IoT networks.