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Title:
An Interactive Approach of Rule Mining and Anomaly Detection for Internal Risks
Author: Liu Kun(刘堃)1,2; Wu, Yunkun1,2; Wei, Wenting3; Wang, Zhonghui4; Zhu, Jiaqi2; Wang, Hongan2
Conference Name: 6th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2020
Conference Date: 2020-7-16~2020-7-17
Issued Date: 2020-11-17
Conference Place: Istanbul, Turkey (Online)
DOI: 10.1007/978-981-15-8603-3_32
Keyword: Internal risks ; Behavior rule mining ; Anomaly detection ; Complex events
Related URLs: 查看原文
Publisher: Springer Science and Business Media Deutschland GmbH
Publish Place: Singapore
Indexed Type: EI
ISSN: 21945357
ISBN: 9789811586026
Sponsorship: 2018YFC0116703
English Abstract:

How to prevent internal risks to the information system, especially for undefined risks, is a great challenge. A reasonable approach is to mine the behavior rules of internal staff on historical data through various data mining algorithms and then use the behavior rules to detect abnormal behaviors. However, in practice, risk control officers are often not familiar with data mining technologies, so it is hard to make them effectively choose and adapt these algorithms to find internal risks. In this paper, we propose an interactive approach for behavior rule mining and anomaly detection. Firstly, we express behavior rules and abnormal behaviors as complex events uniformly to accommodate different mining algorithms. Then, the internal staff’s history behavior logs generated during production are used for mining behavior rules. Next, mined behavior rules are applied to new logs for anomaly detection. Finally, the detected abnormal behavior will be reported to the risk control officer for evaluation, and the feedback will be used for improving mining and detection settings to form a gradual and interactive process. The experiments on the real production data show that the approach is effective and efficient to detect abnormal behavior and can be used to prevent internal risks of the information system of big corporations such as banks.

Language: 英语
Citation statistics:
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/19328
Appears in Collections:人机交互技术与智能信息处理实验室_会议论文

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description.institution: 1.University of Chinese Academy of Sciences, Beijing; 100049, China
2.Institute of Software Chinese Academy of Sciences, Beijing; 100190, China
3.China Development Bank, Beijing; 100031, China
4.State Grid Liaoning Electric Power Supply Co. Ltd., Shenyang; 110006, China

Recommended Citation:
Liu, Kun,Wu, Yunkun,Wei, Wenting,et al. An Interactive Approach of Rule Mining and Anomaly Detection for Internal Risks[C]. 见:6th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2020. Istanbul, Turkey (Online). 2020-7-16~2020-7-17.https://link.springer.com/chapter/10.1007/978-981-15-8603-3_32.
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