A Novel Intrusion Detection Architecture for the Internet of Things (IoT) with Knowledge Discovery and Sharing

The super data transmission capability and connectivity of wireless technologies have promoted the arrival of the Internet of Things (IoT) era. However, the distinct characteristics of IoT devices make them vulnerable to malicious attacks such as hackers and viruses. This paper designs a novel IoT intrusion detection architecture that combines knowledge extraction and sharing, which can extract human understandable knowledge from the trained deep learning model and apply it to the training process of the detection model. The obtained knowledge can also be shared with other detection systems based on the blockchain, which will effectively improve the intrusion detection capabilities of the IoT and realize collective learning. In addition, we propose a CNN-based semi-supervised learning method under the constraints of rules, which can effectively alleviate the catastrophic interference generated during the self-training process and improve detection accuracy. Simulation results confirm the effectiveness of the proposed architecture and method.