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Deep Learning for Detection of BGP Anomalies

  • Marijana CosovicEmail author
  • Slobodan Obradovic
  • Emina Junuz
Conference paper
  • 1.1k Downloads
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

The Internet uses Border Gateway Protocol (BGP) for exchange of routes and reachability information between Autonomous Systems (AS). Hence, BGP is subject to anomalous traffic that can cause problems with connectivity and traffic loss. Routing Table Leak (RTL), worm and power outage events are considered anomalous in the sense that they can disrupt the Internet routing and cause slowdowns of varying severity, which leads to packet delivery reliability issues. Deep learning, a subfield of machine learning, could be applied in detection of BGP anomalies. Studying RTL, worm, and power outage events are of interest to network operators and researchers alike. In this paper, we consider datasets of several events, all of which caused large-scale Internet outages. We use artificial neural network (ANN) models based on a backpropagation algorithm for anomalous event classification.

Keywords

Machine learning Deep learning Anomaly detection BGP Sampling 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marijana Cosovic
    • 1
    Email author
  • Slobodan Obradovic
    • 1
  • Emina Junuz
    • 2
  1. 1.Faculty of Electrical EngineeringUniversity of East SarajevoIstocno SarajevoBosnia and Herzegovina
  2. 2.Faculty of Information TechnologyDzemal Bijedic UniversityMostarBosnia and Herzegovina

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