Double-Optimized CS-BP Anomaly Prediction for Control Operation Data

Abstract

Automation control, which is one functional core of industrial control system, has become the prime attack target due to its vulnerabilities. Furthermore, many industrial cyber threats can disturb or destroy the correctness of control operation data to cause industrial accidents, when one normal production process is running smoothly and orderly. In order to effectively identify abnormal activities in various control operation data, this paper proposes one BP (Back Propagation) neural network anomaly prediction model based on the double-optimized CS (Cuckoo Search) algorithm. By using the exponential decline strategy and Gaussian perturbation to improve the traditional CS algorithm, this model can obtain one effective anomaly prediction engine based on the optimized BP neural network: for one thing, it can quickly enter the local search through the exponential decline strategy; for another, the information exchange between all local positions and the global optimal positions is realized by Gaussian perturbation. Moreover, the double-optimized CS algorithm not only solves the problem that the traditional BP neural network is prone to fall into local optimal solution, but also eliminates the defect of low vitality in the traditional CS algorithm. Consequently, this model can realize the high-precision prediction of abnormal control operation data. The experimental results show that, compared with other approaches, this model has better prediction performance under both normal and attack states, and can ensure the security of automation control in industrial production.