Trustworthy Network Anomaly Detection Based on an Adaptive Learning Rate and Momentum in IIoT

Abstract

While the industrial Internet of Things (IIoT) brings convenience to the industry, it also brings security problems. Due to the massive amount of data generated by the surge of IIoT devices, it is impossible to ensure whether these data contain an attack or untrustworthy data, therefore, how to ensure the security and trustworthiness of IIoT devices has become an urgent problem to solve. In this article, we design a new hinge classification algorithm based on mini-batch gradient descent with an adaptive learning rate and momentum (HCA-MBGDALRM) to minimize the effects of security attacks. The algorithm significantly improves the performance of deep network training compared with traditional neural networks, decision trees and logistic regression in terms of scale and speed. In addition, we have solved the data skew problem in the shuffle phase, and we implement a parallel framework for HCA-MBGDALRM to accelerate the processing speed of very large traffic data sets.