One Intrusion Detection Method Based On Uniformed Conditional Dynamic Mutual Information

被引：5

作者：

Lu, Liangfu ^{[1
]}

Zhu, Xinhe ^{[2
]}

Zhang, Xuyun ^{[3
]}

Liu, Junhan ^{[1
]}

Bhuiyan, Md Zakirul Alam ^{[4
]}

Cui, Guangtai ^{[5
]}

机构：

[1] Tianjin Univ, Sch Math, Tianjin 300072, Peoples R China

[2] Tianjin Polytech Univ, Sch Sci, Tianjin 300387, Peoples R China

[3] Univ Auckland, Dept Elect & Comp Engn, Auckland 1142, New Zealand

[4] Fordham Univ, Dept Comp & Informat Sci, Bronx, NY 10458 USA

[5] Hohai Univ, Dept Math, Nanjing 211100, Jiangsu, Peoples R China

来源：

2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE) | 2018年

关键词：

Feature Selection; SVM; Intrusion Detection; Mutual information; FEATURE-SELECTION;

D O I：

10.1109/TrustCom/BigDataSE.2018.00170

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.

引用

页码：1236 / 1241

页数：6

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