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2018 | OriginalPaper | Chapter

Data Stream Clustering Algorithm Based on Bucket Density for Intrusion Detection

Authors : Chunyong Yin, Lian Xia, Jin Wang

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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Abstract

The ability to process data streams has become one of the challenges of the current intrusion detection systems. A data stream clustering algorithm based on bucket density is proposed for this situation which is able to identify clusters in any shapes and the speed of online layer is fast. Feedback principle is used to solve the problem that some of the edge of the bucket is lost and users does not need to specify the number of clusters. An intrusion detection system is constructed with the improved algorithm. The experiment shows that the algorithm proposed has fast speed for clustering. The system based on the algorithm has a better capability of detection.

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Literature
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Yazdan, J., Hossein, N.: A lattice based nearest neighbor classifier for anomaly intrusion detection. J. Adv. Comput. Res. 4(4), 51–60 (2013)
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Cassisi, C., Ferro, A., Giugno, R., et al.: Enhancing density-based clustering: parameter reduction and outlier detection. Inf. Syst. 38(3), 317–330 (2013)CrossRef
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Aggarwal, C.C., Han, J.W., Wang, J.Y., et al.: A framework for clustering evolving data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases, VLDB Endowment, Berlin, pp. 81–92 (2003)
Metadata
Title
Data Stream Clustering Algorithm Based on Bucket Density for Intrusion Detection
Authors
Chunyong Yin
Lian Xia
Jin Wang
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-7604-6

Electronic ISBN: 978-981-10-7605-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7605-3_134