IoT Devices Security Improvement Based on Collaborative Intrusion Detection System and Blockchain Technology

IoT is one of today's major growth areas. With its low computing power and massively exchanged heterogeneous data, it is vulnerable to numerous at-tacks. In this paper, we proposed a collaborative system based on hybrid intrusion detection and blockchain technology to improve IoT devices security. Thus, we simulated a federated learning using Deep Neural Network, Flower library and CICIoT2023 dataset to detect and classify IoT network traffic as malicious or benign for zero-day attack whose signatures are not yet known and for those whose signatures are already known, we use signature-based IDS. The CICIoT2023 dataset used was divided into ten (10) samples using StratifiedKFold methods and each sample was assigned to a client. The results showed the effectiveness of our approach, with an accuracy around 98.8%. At last, we developed and deployed a smart contract on Solidity to simulate the dissemination of alerts between blockchain nodes.