Abstract
For a large number of network attacks, feature selection is used to improve intrusion detection efficiency. A new mutual information algorithm of the redundant penalty between features (RPFMI) algorithm with the ability to select optimal features is proposed in this paper. Three factors are considered in this new algorithm: the redundancy between features, the impact between selected features and classes and the relationship between candidate features and classes. An experiment is conducted using the proposed algorithm for intrusion detection on the KDD Cup 99 intrusion dataset and the Kyoto 2006+ dataset. Compared with other algorithms, the proposed algorithm has a much higher accuracy rate (i.e., 99.772%) on the DOS data and can achieve better performance on remote-to-login (R2L) data and user-to-root (U2R) data. For the Kyoto 2006+ dataset, the proposed algorithm possesses the highest accuracy rate (i.e., 97.749%) among the other algorithms. The experiment results demonstrate that the proposed algorithm is a highly effective feature selection method in the intrusion detection.
Highlights
Along with network the development and application, serious security threats have emerged.Intrusion detection based on networks is an important step of cyber security [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
In Reference [21], the improved mutual information feature selection (MIFS-U) was described but it does not consider the redundancy between candidate features and classes
The datasets used in this paper are the Knowledge Discovery and Data Mining (KDD) Cup
Summary
Along with network the development and application, serious security threats have emerged. A feature selection method based on deep learning is shown for intrusion detection in Reference [6], which can improve the detection rate and reduce the false positive rate. In Reference [21], the improved mutual information feature selection (MIFS-U) was described but it does not consider the redundancy between candidate features and classes. A weakness of the already reported feature selection algorithms is that they only consider the part of penalty factors to affect the feature subset selection and intrusion detection efficiency.
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