Effective traffic features selection algorithm for cyber-attacks samples
Abstract
By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.
- Publication:
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6th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation (CDMMS 2018)
- Pub Date:
- May 2018
- DOI:
- 10.1063/1.5039141
- Bibcode:
- 2018AIPC.1967d0067L