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Against Insider Threats with Hybrid Anomaly Detection with Local-Feature Autoencoder and Global Statistics (LAGS)

Minhae JANG, Yeonseung RYU, Jik-Soo KIM, Minkyoung CHO

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Summary :

Internal user threats such as information leakage or system destruction can cause significant damage to the organization, however it is very difficult to prevent or detect this attack in advance. In this paper, we propose an anomaly-based insider threat detection method with local features and global statistics over the assumption that a user shows different patterns from regular behaviors during harmful actions. We experimentally show that our detection mechanism can achieve superior performance compared to the state of the art approaches for CMU CERT dataset.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.4 pp.888-891
Publication Date
2020/04/01
Publicized
2020/01/10
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDL8180
Type of Manuscript
LETTER
Category
Dependable Computing

Authors

Minhae JANG
  KEPCO Research Institute
Yeonseung RYU
  Myongji University
Jik-Soo KIM
  Myongji University
Minkyoung CHO
  Myongji University

Keyword