Improving anomaly detection in BGP time-series data by new guide features and moderated feature selection algorithm

Abstract

The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide essential routing information where abnormal routing behavior impairs global Internet connectivity and stability. Hence, employing anomaly detection algorithms is important for improving the performance of BGP routing protocol. In this paper, we propose two algorithms; the first is the guide feature generator (GFG), which generates guide features from traditional features in BGP time-series data using moving regression in combination with smoothed moving average. The second is a modified random forest feature selection algorithm which is employed to automatically select the most dominant features (ASMDF). Our mechanism shows that the detected anomalies are more realistic and the selected features are generally consistent across time series. Experimental evaluations using multiple machine learning models reveal that the proposed algorithms achieve up to 32.36% improvement in accuracy rate, up to 35.44% reduction in false negative rate, and up to 43.99% reduction in false positive rate compared to not using these algorithms. Moreover, the ASMDF option increases the feature selection speed more than 3 times compared to most existing feature selection algorithms.

Investigation on the Identity Construction of Young Foreign Language Teachers in Colleges and Universities Based on Feature Selection Algorithm

“…In the formula, q j represents the weight of the sample, q xj u represents the weight of the sample with the sample number j whose samples are dissimilar, and q yj u represents the weight of the sample with similarity. It can be seen from the formula that the size of the weights and the degree of correlation of the sample features are positively correlated [22].…”