File Tracking and Visualization Methods Using a Network Graph to Prevent Information Leakage
Author:
Affiliation:
1. Intelligent Systems Laboratory, SECOM Co., Ltd.
2. Graduate School of Engineering, Kobe University
3. SECOM Science and Technology Foundation
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E106.D/9/E106.D_2022ICP0014/_pdf
Reference39 articles.
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2. [2] IBM Security, “Cost of a data breach report,” 2020. 10.1016/s1361-3723(21)00082-8
3. [3] S. Mehnaz and E. Bertino, “Ghostbuster: A fine-grained approach for anomaly detection in file system accesses,” Proc. Seventh ACM on Conference on Data and Application Security and Privacy, CODASPY '17, New York, NY, USA, pp.3-14, Association for Computing Machinery, 2017. DOI:10.1145/3029806.3029809 10.1145/3029806.3029809
4. [4] C. Gates, N. Li, Z. Xu, S.N. Chari, I. Molloy, and Y. Park, “Detecting insider information theft using features from file access logs,” European Symposium on Research in Computer Security, vol.8713, pp.383-400, Springer, Cham, 2014. DOI:10.1007/978-3-319-11212-1_22 10.1007/978-3-319-11212-1_22
5. [5] F. Toffalini, I. Homoliak, A. Harilal, A. Binder, and M. Ochoa, “Detection of masqueraders based on graph partitioning of file system access events,” 2018 IEEE Security Privacy Workshops (SPW), pp.217-227, 2018. DOI:10.1109/SPW.2018.00037 10.1109/spw.2018.00037
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