An industrial control system malicious sample generation method based on adversarial learning
An industrial control system, anti-sample technology, applied in general control systems, neural learning methods, control/regulation systems, etc., can solve problems such as hidden dangers of industrial control system security, inability to normally identify malicious traffic, etc., to enhance security performance, prevent Attacks, the effect of increasing security
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[0030] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0031] The invention provides a method for generating malicious samples of industrial control systems based on confrontation learning, such as figure 1 shown, including the following steps:
[0032] (1) The adversarial sample generator sniffs the communication data of the industrial control system, obtains the communication data with the same distribution as the training data used by the industrial control intrusion detection system, and labels the communication data with category labels, including abnormal and normal, among which The abnormal communication data is used as the original attack sample. The industrial control intrusion detection system is an existing industrial control intrusion detection system based on machine learning method.
[0033] Among them, the adversarial sample generator should be a black-box attacker, and can...
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