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Ensemble Strategy for Insider Threat Detection from User Activity Logs

Shihong Zou1, Huizhong Sun1, *, Guosheng Xu1, Ruijie Quan2

1 School of Cyberspace Security, Beijing University of Posts and Telecommunication, Beijing, China.
2 Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia.

* Corresponding Author: Huizhong Sun. Email: email.

Computers, Materials & Continua 2020, 65(2), 1321-1334. https://doi.org/10.32604/cmc.2020.09649

Abstract

In the information era, the core business and confidential information of enterprises/organizations is stored in information systems. However, certain malicious inside network users exist hidden inside the organization; these users intentionally or unintentionally misuse the privileges of the organization to obtain sensitive information from the company. The existing approaches on insider threat detection mostly focus on monitoring, detecting, and preventing any malicious behavior generated by users within an organization’s system while ignoring the imbalanced ground-truth insider threat data impact on security. To this end, to be able to detect insider threats more effectively, a data processing tool was developed to process the detected user activity to generate informationuse events, and formulated a Data Adjustment (DA) strategy to adjust the weight of the minority and majority samples. Then, an efficient ensemble strategy was utilized, which applied the extreme gradient boosting (XGBoost) model combined with the DA strategy to detect anomalous behavior. The CERT dataset was used for an insider threat to evaluate our approach, which was a real-world dataset with artificially injected insider threat events. The results demonstrated that the proposed approach can effectively detect insider threats, with an accuracy rate of 99.51% and an average recall rate of 98.16%. Compared with other classifiers, the detection performance is improved by 8.76%.

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Cite This Article

S. Zou, H. Sun, G. Xu and R. Quan, "Ensemble strategy for insider threat detection from user activity logs," Computers, Materials & Continua, vol. 65, no.2, pp. 1321–1334, 2020.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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