Research on Abnormal Detection Based on Improved Combination of K - means and SVDD | |
Hao Xiaohong1; Zhang Xiaofeng2 | |
2018 | |
卷号 | 114 |
DOI | 10.1088/1755-1315/114/1/012014 |
英文摘要 | In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample. In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method. |
会议录 | 2017 INTERNATIONAL CONFERENCE ON POWER AND ENERGY ENGINEERING
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会议录出版者 | IOP PUBLISHING LTD |
会议录出版地 | DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND |
语种 | 英语 |
WOS研究方向 | Energy & Fuels ; Engineering |
WOS记录号 | WOS:000454981000014 |
内容类型 | 会议论文 |
源URL | [http://119.78.100.223/handle/2XXMBERH/36131] ![]() |
专题 | 电气工程与信息工程学院 |
通讯作者 | Hao Xiaohong |
作者单位 | 1.Lanzhou Univ Technol, Sch Elect Engn & Informat Engn, Lanzhou 730050, Gansu, Peoples R China 2.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China |
推荐引用方式 GB/T 7714 | Hao Xiaohong,Zhang Xiaofeng. Research on Abnormal Detection Based on Improved Combination of K - means and SVDD[C]. 见:. |
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