ISP Self-Operated BGP Anomaly Detection Based on Weakly Supervised Learning

Abstract

The Border Gateway Protocol (BGP) is arguably the most important and irreplaceable protocol in the network. However, the lack of routing authentication and validation makes it vulnerable to attacks, including routing leaks, route hijacking, prefix hijacking, etc. Therefore, in this paper we propose a generalized framework for ISP self-operated BGP anomaly detection based on weakly supervised learning. To tackle the problem of insufficient data in BGP anomaly detection, we propose an approach to learn from the other anomaly detection systems through knowledge distillation. To reduce the impact of inaccurate supervision, we design a self-attention-based Long Short-Term Memory (LSTM) model to self-adaptively mine the differences between BGP anomaly categories, including both feature and time dimensions. Finally, we implement a system and demonstrate the performance through a set of comprehensive experiments. Compared with the state-of-the-art schemes, our scheme has better generalization on various anomaly types.