Performance Evaluation of Decentralized Machine Learning based Network-Based Intrusion Detection System for Internet of Things

Abstract

In recent years, IoT applications have become increasingly popular. Smart services have been deployed from the IoT infrastructure to provide convenience for humans in their lives and related activities. Alongside IoT’s potential, security and privacy concerns have been highlighted in the IoT architecture. One weakness of the IoT system is the widespread deployment of sensor nodes with wireless connections. Additionally, the limited resources of these sensor nodes pose a challenge in designing and implementing security solutions for the IoT infrastructure. In this article, we plan to deploy a Network Intrusion Detection System (NIDS) for IoT infrastructure. This system is towards to runs on the Swarm Learning framework, which supports decentralized machine learning models to ensure data distribution during training. This framework also operates on Ethereum – an open-source blockchain platform - to ensure authenticity and security while training decentralized machine learning models. We experiment with various scenarios using the DNN model via the CiCIoT2023 dataset and the CiCIoMT24 dataset. The results demonstrate that our proposed system ensures accuracy comparable to centralized machine learning and Federated Learning models. In addition, we also tested and evaluated based on training time and resource usage, thereby concluding that the cost and effectiveness of the Swarm Learning system is better than Federated Learning.