Border Gateway Protocol Anomaly Detection Using Neural Network

Mohsen Karimi, Ali Jahanshahi, Abbas Mazloumi, Hadi Zamani Sabzi

Abstract: Having reliable and stable connectivity to the Internet dramatically depends on how Border Gateway Protocol (BGP) can avoid bad-behaviour events by detecting them on time. Despite a lot of efforts have gone into detecting BGP anomalies during the last decade, it is still a challenging issue due to emerging new abnormal behaviours both from the attackers and network misconfigurations. In this work, we propose a Neural Network classifier to detect the abnormal BGP events caused by worm attacks in the network. The results show that our method outperforms the previous work in both generality and accuracy.

External IDs:dblp:conf/bigdataconf/KarimiJMS19