An Early Warning Model for Industrial Network Security Issues: A Crafted Strategy for High Accuracy Based on Machine Learning Approach

Abstract

An industrial network has become an important infrastructure. As industrial networks develop, their cybersecurity problems become more and more prominent. The attacks currently realized to networks turn out to be advancing quicker than ever, and their destructive force also continuously gets bigger. Thus, the available early warning technology for industrial network security issues requires more accuracy and timeliness since a serious amount of delays occurs in real cases. The article proposes a strategy with high accuracy based on a machine-learning algorithm. Nonlinear high-dimensional data with different feature characteristics in cyber-attacks and low training efficiency of conventional early warning models to predict attacks are underlined as a significant part of the problem to deal with. Thus, the manuscript suggests a feature selection method based on the Tuna Swarm Optimization (TSO) algorithm to filter out redundant features and reduce the data’s dimensionality. Then, the Extreme Learning Machine (ELM) and Auto-Encoder (AE) are combined to construct the model called Extreme Learning Machine-Auto Encoder (ELM-AE) to be implemented as the basis of the early warning model for industrial network security. Afterward, the improved Whale Optimization Algorithm (I-WOA) is used to optimize the parameters of the ELM, to construct the obtained optimization model. Finally, the obtained optimization model is applied to detect attacks on industrial cyber security systems as an early warning method. Eventually, the proposed model is tested by constructing an evaluation index system on how effective the early warning system functions. The experimental results show that the proposed warning model for industrial network security issues has high warning accuracy and efficiency concurrently, which provides an advanced early warning model for network attacks. The proposed model with 92.64% precision and 51.84 s average execution time excels over other methods.