A Novel Internet of Things (IoT) Web Attack Detection Architecture Based on the Combination of Symbolism and Connectionism AI

The rapid advancement and wide application of the Internet of Things technology (IoT) have brought unprecedented convenience to people’s production and life. A great number of devices are connected to the IoT network to provide various services for people, which also makes the IoT more vulnerable to various cyber-attacks. This paper designs a novel IoT web attack detection architecture, which combines the powerful knowledge expression ability and high interpretability of symbolic artificial intelligence (AI) with the adaptive learning ability of connectionist AI to form a closed loop of knowledge embedding and extraction, effectively improve the detection ability of web attacks. The architecture solves the “black box” feature of deep learning models and can obtain knowledge from the trained detection model and add it to the training process of the new model to improve detection capabilities. It also uses the advantages of blockchain technology to realize intelligent sharing between different detection systems, solve the problem of difficult detection model updates and training data acquisition “bottlenecks”. To better detect web attacks, we propose a semi-supervised learning method based on an interpretable convolutional neural network (CNN) to reduce misjudgments during self-training and improve detection accuracy. Additionally, we propose a new feature method to extract the features of web logs in IoT devices, which can help the system to detect web attacks in IoT more quickly and accurately. Simulation results on two different datasets show that the proposed architecture and method can effectively detect web attacks in IoT and reduce the false positive rate.