Abstract
In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.
Highlights
Intrusion detection system (IDS)[1] prevent or mitigate threats from network attacks without affecting network performance
How to improve the detection speed of IDS under the premise of correct detection has become the hot spot of current research
Studies [2] have shown that the redundancy features in access information are the main reason for the slowdown of IDS
Summary
Intrusion detection system (IDS)[1] prevent or mitigate threats from network attacks without affecting network performance. The detection speed is too slow, which may lead to an intrusion situation. Studies [2] have shown that the redundancy features in access information are the main reason for the slowdown of IDS. Many researchers [1,2,3,4,5,6] select the core features that can be identified by selecting the features of access information. By selecting features of the access information, retaining important features that can represent the access information is an effective method to improve the detection speed. Based on the above description, this paper proposes a feature selection algorithm combining information gain and multi-objective genetic search. C4.5 is used to test the data after feature selection to verify the effectiveness of the method.
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