Data-Driven Model-Based Detection of Malicious Insiders via Physical Access Logs
Title | Data-Driven Model-Based Detection of Malicious Insiders via Physical Access Logs |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Carmen Cheh, University of Illinois at Urbana-Champaign, Binbin Chen, Advanced Digital Sciences Center, Singapore, William G. Temple, A, Advanced Digital Sciences Center, Singapore, William H. Sanders, University of Illinois at Urbana-Champaign |
Conference Name | 14th International Conference on Quantitative Evaluation of Systems (QEST 2017) |
Date Published | September 2017 |
Publisher | Springer International Publishing |
Conference Location | Berlin, Germany |
Keywords | Cyber-physical systems, insider threat, Intrusion detection, physical access, Physical movement, science of security, user behavior |
Abstract | The risk posed by insider threats has usually been approached by analyzing the behavior of users solely in the cyber domain. In this paper, we show the viability of using physical movement logs, collected via a building access control system, together with an understanding of the layout of the building housing the system's assets, to detect malicious insider behavior that manifests itself in the physical domain. In particular, we propose a systematic framework that uses contextual knowledge about the system and its users, learned from historical data gathered from a building access control system, to select suitable models for representing movement behavior. We then explore the online usage of the learned models, together with knowledge about the layout of the building being monitored, to detect malicious insider behavior. Finally, we show the effectiveness of the developed framework using real-life data traces of user movement in railway transit stations. |
URL | https://link.springer.com/chapter/10.1007/978-3-319-66335-7_17#aboutcontent |
DOI | https://doi.org/10.1007/978-3-319-66335-7_17 |
Citation Key | node-37484 |
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