TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
A Classification Algorithm Based on Data Clustering and Data Reduction for Intrusion Detection System over Big Data |
| 영문제목(English Title) |
A Classification Algorithm Based on Data Clustering and Data Reduction for Intrusion Detection System over Big Data |
| 저자(Author) |
Qiuhua Wang
Xiaoqin Ouyang
Jiacheng Zhan
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| 원문수록처(Citation) |
VOL 13 NO. 07 PP. 3714 ~ 3732 (2019. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.
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| 키워드(Keyword) |
IDS
KNN
Mini Batch K-Means
Representative Instance
Clustering
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| 파일첨부 |
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