Detecting BGP Anomalies Using Machine Learning Techniques

被引：0

作者：

Ding, Qingye ^{[1
]}

Li, Zhida ^{[1
]}

Batta, Prerna ^{[1
]}

Trajkovic, Ljiljana ^{[1
]}

机构：

[1] Simon Fraser Univ, Vancouver, BC, Canada

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2016年

关键词：

Border gateway protocol; routing anomalies; machine learning; feature selection; support vector machine; long short-term memory;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Border Gateway Protocol (BGP) anomalies affect network operations and, hence, their detection is of interest to researchers and practitioners. Various machine learning techniques have been applied for detection of such anomalies. In this paper, we first employ the minimum Redundancy Maximum Relevance (mRMR) feature selection algorithms to extract the most relevant features used for classifying BGP anomalies and then apply the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms for data classification. The SVM and LSTM algorithms are compared based on accuracy and F-score. Their performance was improved by choosing balanced data for model training.

引用

页码：3352 / 3355

页数：4