Detection of Fake Data Injection Attack in the Industrial Process Based on Multi-Layer Support Vector Machines
With the widespread application of internet technology in various industrial processes,industrial control systems are increasingly likely to be subject to malicious cyber-attacks.Therefore,a network attack detection scheme can be designed to enhance the network security defense capability in the industrial processes,and to effectively reduce the losses caused by malicious attacks.This study established a physical model of industrial process network attacks and proposed corresponding detection algorithms to detect abnormal conditions caused by cyber-attacks in real-time.This study proposes a multi-layer support vector machine method for detecting false data injection attacks in chemical production Tennessee-Eastman process simulation network attacks.This algorithm uses recursive feature elimination methods to establish a binary support vector machine model for multiple types of attacks,and forms a multi-layer support vector machine model through fusion decision-making.
industrial processesnetwork securityfake data injection attacksmultilayer support vector machine
万方数据
维普
