Aim. The purpose of this study is how to better detect attack traffic in imbalance datasets. The deep learning technology has played an important role in detecting malicious network traffic in recent years. However, it suffers serious imbalance distribution of data if the traffic model skews towards the modeling in the benign direction, because only a small portion of traffic is malicious, while most network traffic is benign. That is the reason why the authors wrote this manuscript. Methods. We propose a cost-sensitive approach to improve the HTTP traffic detection performance with imbalanced data and also present a character-level abstract feature extraction approach that can provide features with clear decision boundaries in addition. Finally, we design a spark-based HTTP traffic detection system based on these two approaches. Results. The methods proposed in this paper work well in imbalanced datasets. Compared to other methods, the experiment results indicate that our system has F1-score in a high precision. Conclusion. For imbalanced HTTP traffic detection, we confirmed that the method of feature extraction and the cost function is very effective. In the future, we may focus on how to use the cost function to further improve detection performance.
Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data
