Cost-sensitive random GBDT based anomaly detection method for cloud platform traffic data

Liebin Yu; Ruini Wang

doi:10.1117/12.2681581

1 June 2023 Cost-sensitive random GBDT based anomaly detection method for cloud platform traffic data

Liebin Yu, Ruini Wang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271810 (2023) https://doi.org/10.1117/12.2681581
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

In order to solve the problem of low classification accuracy of the cloud platform anomaly detection model, this paper proposes a cost-sensitive random GBDT anomaly detection method, considering the different classification costs of different types of data. In this paper, a cost- sensitive stochastic GBDT-based anomaly detection method is proposed based on considering the different classification costs of different categories of data. The method first introduces the cost matrix into the design of the loss function to establish a cost-sensitive loss function to reduce the classification cost of different categories of data; and in order to reduce the computational effort and improve the computational speed, the idea of stochastic gradient boosting is introduced into the GBDT method. And the effectiveness of the method proposed in this paper is proved by simulation experiments.

Citation Download Citation

Liebin Yu and Ruini Wang "Cost-sensitive random GBDT based anomaly detection method for cloud platform traffic data", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271810 (1 June 2023); https://doi.org/10.1117/12.2681581

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