Efficient BGP Intrusion Detection Model Using Machine Learning: A Comparative Study with AdaBoost as the Optimal Classifier

The Border Gateway Protocol (BGP) is a crucial component of the Internet's infrastructure that enables the exchange of routing information among multiple Autonomous Systems so data flow from one network to another becomes possible. However, rare anomalies in BGP, such as IP prefix hijacks, misconfigurations, and worm attacks, when they occur, can cause significant disruptions to the network and threaten the stability and reliability of the Internet. Considerable efforts have been made to understand the nature of normal and abnormal BGP updates to identify and mitigate their disruptive consequences. Recent studies in the literature suggest that machine learning (ML) techniques can achieve a high level of accuracy and robustness in anomaly detection. To fully leverage the advantages of ML techniques, it is necessary to pre-process the data and choose a suitable model that helps identify and mitigate against any such BGP anomalies and improve the stability and reliability of the Internet. This paper evaluates multiple machine learning models for detecting BGP anomalies and comprehensively analyzes their effectiveness. Results reveal that AdaBoost achieves an impressive accuracy of 97.22%, making it the optimal choice for BGP anomaly detection.