The classification of Border Gateway Protocol (BGP) anomalies is essential for maintaining Internet stability and security, as such anomalies can impair network functionality and reliability. Previous studies has examined the impact of key features on anomaly detection; however, current methodologies frequently demonstrate high computational costs, complexity, and usage challenges.
In networks characterized by imbalanced traffic, detecting malicious cyber-attacks poses a significant challenge due to their ability to blend seamlessly with regular data volumes. This creates a formidable hurdle for Network Intrusion Detection Systems (NIDS) striving for accurate and timely identification. The imbalance in normal and attack data, coupled with the diversity among attack categories, complicates intrusion detection. This research proposes a novel approach to address this issue by combining Extreme Gradient Boosting with variational autoencoder (XIDINT