A self-adaptive updating network intrusion detection method
A network intrusion detection and self-adaptive update technology, applied in the field of cyberspace security, can solve problems such as inability to adapt to changes in the environment, lack of automatic update learning, and inability to automatically trigger the automatic update of intrusion detection models, etc., to achieve changes that can adapt to the environment , to achieve the effect of adaptive update
Active Publication Date: 2022-03-15
SICHUAN UNIV
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Problems solved by technology
[0007] The purpose of the present invention is to provide an adaptive update network intrusion detection method, which is used to solve the problem that the traditional network intrusion detection method cannot automatically trigger the automatic update of the intrusion detection model, does not have the ability of automatic update and learning, and cannot adapt to changes in the environment question
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Embodiment 1
[0071] combine figure 1 As shown, an adaptive update network intrusion detection method, including:
[0072] Step S100: training multiple classification models in different feature spaces through feature space mapping;
[0073] Step S200: Multiple classification models cooperate to perform intrusion detection;
[0074] Step S300: multiple classification models are automatically updated and triggered according to the difference between the current sample distribution and the historical sample distribution;
[0075] Step S400: Multiple classification models cooperate to perform adaptive update.
[0076] Periodically calculate the relative entropy changes between the newly collected sample distribution and the historical sample distribution, and automatically determine the timing of triggering the update of the intrusion detection model; 2) Through the collaborative learning of multi-classification models, the adaptive update of the intrusion detection model is realized.
[0077...
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The invention discloses an adaptive update network intrusion detection method, through feature space mapping, training multiple classification models in different feature spaces; multiple classification models cooperate to perform intrusion detection; multiple classification models according to the current sample distribution and The difference in historical sample distribution is automatically updated; multiple classification models cooperate to perform adaptive updates. The present invention automatically determines the trigger update timing of the intrusion detection model by periodically calculating the relative entropy changes of the newly collected sample distribution and the historical sample distribution; Adapt to changes in the environment.
Description
technical field [0001] The invention relates to the technical field of network space security, in particular to an adaptive update network intrusion detection method. Background technique [0002] With the advent of the era of big data and artificial intelligence, Internet attack patterns are diversified, large-scale, and intelligent, and the attack duration is longer, faster, and the implementation cost is lower. Most of the attackers operate in groups, and most of them have specific attack targets, mainly targeting the core business systems of enterprises and institutions, and comprehensively using loopholes, social workers, new Trojan horses and other means. [0003] The traditional network intrusion detection uses the machine learning model, and the traditional machine learning model is based on the theory of structural risk minimization. It is believed that as long as the model satisfies the minimization of empirical risk and confidence risk on the training sample set, ...
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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06F21/57
CPCG06N3/08G06F21/57G06N3/045G06F18/22G06F18/2135G06F18/2411
Inventor 陈文顾守珂康明
Owner SICHUAN UNIV