Application of machine learning techniques to detecting anomalies in communication networks: Datasets and feature selection algorithms

Abstract

Detecting, analyzing, and defending against cyber threats is an important topic in cyber security. Applying machine learning techniques to detect such threats has received considerable attention in research literature. Anomalies of Border Gateway Protocol (BGP) affect network operations and their detection is of interest to researchers and practitioners. In this Chapter, we describe main properties of the protocol and datasets that contain BGP records collected from various public and private domain repositories such as Route Views, Réseaux IP Européens (RIPE), and BCNET. We employ various feature selection algorithms to extract the most relevant features that are later used to classify BGP anomalies.