Application of XGBoost to the cyber-security problem of detecting suspicious network traffic events

Łukasz Podlodowski, Marek Kozłowski

2019 W: Proceedings - 2019 IEEE International Conference on Big Data (Big Data) / Roger Barga, Ronay Ak, Kisung Lee, Yuanyuan Tian, Jun Huan, Latifur Khan, Chaitanya Baru, Xiaohua Hu, Yanfang Fanny Ye, Carlo Zaniolo; Piscataway: Institute of Electrical and Electronics Engineers (IEEE), s. 5902-5907

This paper presents an application of XGBoost as a solution for a task associated with the IEEE BigData2019 Cup: Suspicious Network Event Recognition. As has been shown in the paper, the high-quality classification model can be based on independent predictions of each component in the sequence of network traffic events, then analyzed with statistical aggregation functions to generate the final prediction. We also propose the approach to this problem including handling high dimensionality space of IP addresses through encoding octets separately.