Open Access

ARTICLE

Ensemble Strategy for Insider Threat Detection from User Activity Logs

by Shihong Zou, Huizhong Sun, Guosheng Xu, Ruijie Quan

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: .

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

Received 14 January 2020; Accepted 01 June 2020; Issue published 20 August 2020

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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Citations