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.
关键词
工业过程/网络安全/假数据注入攻击/多层支持向量机
Key words
industrial processes/network security/fake data injection attacks/multilayer support vector machine
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