Selection of Effective Features for BGP Anomaly Detection

被引：3

作者：

Arai, Tatsuya ^{[1
]}

Nakano, Kotaro ^{[1
]}

Chakraborty, Basabi ^{[2
]}

机构：

[1] Iwate Prefectural Univ, Software & Informat Sci, Takizawa, Iwate, Japan

[2] Iwate Prefectural Univ, Fac Software & Informat Sci, Takizawa, Iwate, Japan

来源：

2019 IEEE 10TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST 2019) | 2019年

关键词：

Border Gateway Protocol; Anomaly detection; Feature selection; MACHINE LEARNING TECHNIQUES;

D O I：

10.1109/icawst.2019.8923583

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：

Border Gateway Protocol (BGP) is the internet's default protocol for managing connectivity between Autonomous Systems (AS). Anomalies happen to occur time to time and it is a threat to cyber security. There are various types of BGP anomalies and over the years researches have been done for their detection. Here machine learning techniques are used for detection of BGP anomaly from BGP update messages by considering the problem as a two class classification problem. A set of 35 features are extracted from BGP update messages for Slammer, Nimda and Code Red I attacks. The main objective of this study is to find out important features for detection of BGP anomaly. Popular feature selection algorithms, wrapper as well as several filter based algorithms are used for feature ranking. It is found that at most top 10 features are sufficient for the best classification accuracy which is verified by several classifiers.

引用

页码：215 / 220

页数：6

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