A System for Improving Data Leakage Detection based on Association Relationship between Data Leakage Patterns

Min-Ji Seo and Myung-Ho Kim
Volume: 15, No: 3, Page: 520 ~ 537, Year: 2019
10.3745/JIPS.03.0116
Keywords: Apriori Algorithm, Associated Abnormal Behavior List, Comprehensive Leakage Detection Scenario, Convolutional Neural Network, Data Leakage Detection
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Abstract
This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

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Cite this article
IEEE Style
M. S. M. Kim, "A System for Improving Data Leakage Detection based on Association Relationship between Data Leakage Patterns," Journal of Information Processing Systems, vol. 15, no. 3, pp. 520~537, 2019. DOI: 10.3745/JIPS.03.0116.

ACM Style
Min-Ji Seo and Myung-Ho Kim. 2019. A System for Improving Data Leakage Detection based on Association Relationship between Data Leakage Patterns, Journal of Information Processing Systems, 15, 3, (2019), 520~537. DOI: 10.3745/JIPS.03.0116.