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Lookup NU author(s): Dr Stephen McGough, Dr Budi Arief, Dr Carl Gamble, Dr John Brennan, Professor John Fitzgerald, Professor Aad van Moorsel
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© 2015, Innovative Information Science and Technology Research Group. All rights reserved. The insider threat problem is a significant and ever present issue faced by any organisation. While security mechanisms can be put in place to reduce the chances of external agents gaining access to a system, either to steal assets or alter records, the issue is more complex in tackling insider threat. If an employee already has legitimate access rights to a system, it is much more difficult to prevent them from carrying out inappropriate acts, as it is hard to determine whether the acts are part of their official work or indeed malicious. We present in this paper the concept of “Ben-ware”: a beneficial software system that uses low-level data collection from employees’ computers, along with Artificial Intelligence, to identify anomalous behaviour of an employee. By comparing each employee’s activities against their own ‘normal’ profile, as well as against the organisational’s norm, we can detect those that are significantly divergent, which might indicate malicious activities. Dealing with false positives is one of the main challenges here. Anomalous behaviour could indicate malicious activities (such as an employee trying to steal confidential information), but they could also be benign (for example, an employee is carrying out a workaround or taking a shortcut to complete their job). Therefore it is important to minimise the risk of false positives, and we do this by combining techniques from human factors, artificial intelligence, and risk analysis in our approach. Developed as a distributed system, Ben-ware has a three-tier architecture composed of (i) probes for data collection, (ii) intermediate nodes for data routing, and (iii) high level nodes for data analysis. The distributed nature of Ben-ware allows for near-real-time analysis of employees without the need for dedicated hardware or a significant impact on the existing infrastructure. This will enable Ben-ware to be deployed in situations where there are restrictions due to legacy and low-power resources, or in cases where the network connection may be intermittent or has a low bandwidth. We demonstrate the appropriateness of Ben-ware, both in its ability to detect potentially malicious acts and its lowimpact on the resources of the organisation, through a proof-of-concept system and a scenario based on synthetically generated user data.
Author(s): McGough AS, Arief B, Gamble C, Wall D, Brennan J, Fitzgerald J, van Moorsel A, Alwis S, Theodoropoulos G, Ruck-Keene E
Publication type: Article
Publication status: Published
Journal: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
Year: 2015
Volume: 6
Issue: 4
Pages: 3-46
Print publication date: 01/12/2015
Acceptance date: 01/01/1900
ISSN (print): 2093-5374
ISSN (electronic): 2093-5382
Publisher: Innovative Information Science and Technology Research Group
URL: https://doi.org/10.22667/JOWUA.2015.12.31.003
DOI: 10.22667/JOWUA.2015.12.31.003
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