Abnormal Network Traffic Detection Based on Semi-Supervised Machine Learning

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Network intrusions will cause abnormal network traffic flow. The abnormal network traffic detection can be used to identify the network intrusions. The traditional intrusion detection system is based on pattern recognition which only can be used for well-known network attack behavior. Machine learning can be used to abstract the characters of a class of objects. In this paper we use machine learning classifiers to distinguish abnormal network traffic from the normal traffic background. The experiments show that this is efficient to detect new intrusion. In order to increase the accuracy for new attack detection, our scheme select the with high confidence samples from testing set to expand the training set which is a semi supervised strategy.