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http://dx.doi.org/10.13089/JKIISC.2019.29.2.321

A Study on Anomaly Detection Model using Worker Access Log in Manufacturing Terminal PC  

Ahn, Jong-seong (Graduate School of Information Security, Korea University)
Lee, Kyung-ho (Graduate School of Information Security, Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.29, no.2, 2019 , pp. 321-330 More about this Journal
Abstract
Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.
Keywords
Machine Learning; Anomaly Detection; Feature Selection;
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