Intrusion detection for Industrial Internet of Things based on deep learning

Intrusion detection technology can actively detect abnormal behaviors in the network and is important to the security of Industrial Internet of Things (IIOT). However, there are some issues with the current intrusion detection technology for IIOT, such as extreme imbalance in the number of samples of different classes in the dataset, redundant and meaningless features in the samples, and the inability of traditional intrusion detection methods to meet the requirements of detection accuracy in the increasingly complex IIOT. In view of the extreme imbalance of classes, this paper applies the hierarchical clustering algorithm to the under-sampling technology, which reduces the number of majority samples while reducing the loss of information of majority samples, and solves the problem of missing detection and false detection of minority samples caused by sample imbalance. In order to avoid feature redundancy and interference, this paper proposes an optimal feature selection algorithm based on greedy thought. This algorithm can obtain the optimal feature subset of each type of data in the data set, thus eliminating redundant and interfering features. Aiming at the problem of insufficient detection ability of traditional detection methods, this paper proposes a deep neural network intrusion detection model based on the parallel connection of global and local subnetworks. This model obtains the overall benchmark detection of the dataset through the deep neural network, and then strengthens the detection effect of each subclass through the parallel connection of subnetworks, greatly improving the performance of the intrusion detection algorithm. The experimental results show that the method described in this paper can improve the intrusion detection for IIOT.