Multi-layer anomaly detection method based on network traffic
An anomaly detection and network traffic technology, applied in the field of network security, can solve the problem that the detection effect of the anomaly detection classifier cannot be well satisfied, the attack behavior of small traffic cannot be well identified, and the attack behavior cannot be detected well and other problems, to achieve the effect of improving classification accuracy, compact data, and reasonable selection of parameters
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[0038] The present invention will be described in detail below with reference to the drawings and embodiments.
[0039] The invention provides a multi-layer anomaly detection method based on network traffic, which combines linear discriminant analysis method LDA, genetic algorithm, KNN outlier detection algorithm and random forest algorithm, and is a fusion adaptive method.
[0040] The present invention is based on the benchmark data set KDD99, the improved data set NSL_KDD of KDD99, and the NUSW_NB15 data set that is more in line with modern networks. Among them, the NUSW_NB15 data set is a network anomaly detection data set published in 2015 and contains 9 new attacks Type, the data set can better reflect the current network traffic characteristics and attack methods.
[0041] The method of the present invention can be divided into two aspects: data processing and abnormality detection. Data processing mainly uses LDA, genetic algorithm and KNN outlier detection algorithm. First,...
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