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Advanced insider threat detection model to apply periodic work atmosphere

Authors
Oh, JunhyoungKim, Tae HoLee, Kyung Ho
Issue Date
31-3월-2019
Publisher
KSII-KOR SOC INTERNET INFORMATION
Keywords
Insider threat detection; Machine learning; Unsupervised learning; Security; Privacy Behavior
Citation
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.13, no.3, pp.1722 - 1737
Indexed
SCIE
SCOPUS
KCI
Journal Title
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Volume
13
Number
3
Start Page
1722
End Page
1737
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/66586
DOI
10.3837/tiis.2019.03.035
ISSN
1976-7277
Abstract
We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.
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