Abstract
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.
Published Version
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