An efficient intrusion detection system based on support vector machines and gradually feature removal method

被引：231

作者：

Li, Yinhui ^{[1
]}

Xia, Jingbo ^{[1
]}

Zhang, Silan ^{[1
]}

Yan, Jiakai ^{[1
]}

Ai, Xiaochuan ^{[2
]}

Dai, Kuobin ^{[3
]}

机构：

[1] Huazhong Agr Univ, Coll Sci, Wuhan, Hubei, Peoples R China

[2] Navy Engn Univ, Coll Sci, Wuhan, Hubei, Peoples R China

[3] Huanggang Normal Univ, Coll Math & Info Sci, Huanggang, Hubei, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 01期

关键词：

Intrusion detection; Support vector machine; Feature reduction; SELECTION; DESIGN;

D O I：

10.1016/j.eswa.2011.07.032

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The efficiency of the intrusion detection is mainly depended on the dimension of data features. By using the gradually feature removal method, 19 critical features are chosen to represent for the various network visit. With the combination of clustering method, ant colony algorithm and support vector machine (SVM), an efficient and reliable classifier is developed to judge a network visit to be normal or not. Moreover, the accuracy achieves 98.6249% in 10-fold cross validation and the average Matthews correlation coefficient (MCC) achieves 0.861161. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：424 / 430

页数：7

共 21 条

[1]

Amor N. B., 2004, SAC 04 P 2004 ACM S

[2] A CLUSTERING TECHNIQUE FOR SUMMARIZING MULTIVARIATE DATA [J].

BALL, GH ;

HALL, DJ .

BEHAVIORAL SCIENCE, 1967, 12 (02) :153-&

[3] Training ν-support vector classifiers:: Theory and algorithms [J].

Chang, CC ;

Lin, CJ .

NEURAL COMPUTATION, 2001, 13 (09) :2119-2147

[4] Feature deduction and ensemble design of intrusion detection systems [J].

Chebrolu, S ;

Abraham, A ;

Thomas, JP .

COMPUTERS & SECURITY, 2005, 24 (04) :295-307

[5]

CHIMPHLEE W, 2006, INT C HYBR INF TECHN

[6] An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks [J].

Depren, O ;

Topallar, M ;

Anarim, E ;

Ciliz, MK .

EXPERT SYSTEMS WITH APPLICATIONS, 2005, 29 (04) :713-722

[7] CARRADS: Cross layer based adaptive real-time routing attack detection system for MANETS [J].

Joseph, John Felix Charles ;

Das, Amitabha ;

Lee, Bu-Sung ;

Seet, Boon-Chong .

COMPUTER NETWORKS, 2010, 54 (07) :1126-1141

[8] A new intrusion detection system using support vector machines and hierarchical clustering [J].

Khan, Latifur ;

Awad, Mamoun ;

Thuraisingham, Bhavani .

VLDB JOURNAL, 2007, 16 (04) :507-521

[9] Wrappers for feature subset selection [J].

Kohavi, R ;

John, GH .

ARTIFICIAL INTELLIGENCE, 1997, 97 (1-2) :273-324

[10] An active learning based TCM-KNN algorithm for supervised network intrusion detection [J].

Li, Yang ;

Guo, Li .

COMPUTERS & SECURITY, 2007, 26 (7-8) :459-467

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