Abstract
On the basis of the research of existing intrusion detection technology, the paper establishes an intrusion detection model based on clustering analysis. It perfects the shortcomings existing in traditional one. Meanwhile, in order to improve the shortages of traditional clustering analysis algorithm k-means that it needs to know the number of clustering at the beginning and it is sensitive to initial clustering center, improved k-means algorithm is put forward. It chooses authority data set KDD Cup1999 in the intrusion detection field as experimental data to verify its performance. The experiments show that this algorithm has higher detection rate and lower false positive rate
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Acknowledgement
Our thanks go to Tangshan Science and Technology Bureau (grant number: 11150201A-19), and Hebei United University (grant number: z200716), which grant us enough fund to support our research. Also, we extend our sincere gratitude to editors, the anonymous reviewers and the sponsor.
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Wei, M., Xia, L., Jin, J., Chen, C. (2014). Research of Intrusion Detection Based on Clustering Analysis. In: Zhong, S. (eds) Proceedings of the 2012 International Conference on Cybernetics and Informatics. Lecture Notes in Electrical Engineering, vol 163. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3872-4_252
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DOI: https://doi.org/10.1007/978-1-4614-3872-4_252
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