Intrusion Detection System for Internet of Things based on a Machine Learning approach

With the application of Internet of Things technology to every aspect of life, the potential damage caused by Internet of things attacks is more serious than for traditional network attacks. Traditional intrusion detection systems do not serve the network environment of the IoT very well, so it is important to study intrusion detection systems suitable for the network environment of the Internet of Things. Researchers have found that the combination of machine learning technologies with an intrusion detection system is an effective way to resolve the drawbacks traditional IDSs have when they are used for IoT. This research involves the design of a novel intrusion detection system and the implementation and evaluation of its analysis model. This new intrusion detection system uses a hybrid placement strategy based on a multi-agent system. The new system consists of a data collection module, a data management module, an analysis module and a response module. For the implementation of the analysis module, this research applies a deep neural network algorithm for intrusion detection. The results demonstrate the efficiency of deep learning algorithms for detecting attacks from the transport layer. Compared with traditional detection methods used in IDSs, the analysis indicates that deep learning algorithms are more suitable for intrusion detection in an IoT network environment.