Improving Intrusion Detection with Adaptive Support Vector Machines

被引：5

作者：

Macek, N. ^{[1
]}

Dordevic, B. ^{[2
]}

Timcenko, V. ^{[2
]}

Bojovic, M. ^{[3
]}

Milosavljevic, M. ^{[4
]}

机构：

[1] Sch Elect Engn & Comp Appl Studies, Belgrade 11000, Serbia

[2] Inst Mihailo Pupin Doo, Belgrade 11060, Serbia

[3] IT011, Belgrade 11070, Serbia

[4] Singidunum Univ, Belgrade 11000, Serbia

来源：

ELEKTRONIKA IR ELEKTROTECHNIKA | 2014年 / 20卷 / 07期

关键词：

Intrusion detection; machine learning; support vector machines; false negative rate; SELECTION;

D O I：

10.5755/j01.eee.20.7.8025

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The research topic that this paper is focused on is intrusion detection in critical network infrastructures, where discrimination of normal activity can be easily corrected, but no intrusions should remain undetected. The intrusion detection system presented in this paper is based on support vector machines that classify unknown data instances according both to the feature values and weight factors that represent importance of features towards the classification. The major contribution of the proposed model is significantly decreased false negative rate, even for the minor categories that have a very few instances in the training set, indicating that the proposed model is suitable for aforementioned environments.

引用

页码：57 / 60

页数：4

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