Validating an insider threat detection system: A real scenario perspective

被引:6
作者
Agrafiotis, Ioannis [1 ]
Erola, Arnau [1 ]
Happa, Jassim [1 ]
Goldsmith, Michael [1 ]
Creese, Sadie [1 ]
机构
[1] Univ Oxford, Dept Comp Sci, Oxford, England
来源
2016 IEEE SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (SPW 2016) | 2016年
关键词
Insider threat; anomaly detection; real world case study; Machine learning;
D O I
10.1109/SPW.2016.36
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
There exists unequivocal evidence denoting the dire consequences which organisations and governmental institutions face from insider threats. While the in-depth knowledge of the modus operandi that insiders possess provides ground for more sophisticated attacks, organisations are ill-equipped to detect and prevent these from happening. The research community has provided various models and detection systems to address the problem, but the lack of real data due to privacy and ethical issues remains a significant obstacle for validating and designing effective and scalable systems. In this paper, we present the results and our experiences from applying our detection system into a multinational organisation, the approach followed to abide with the ethical and privacy considerations and the lessons learnt on how the validation process refined the system in terms of effectiveness and scalability.
引用
收藏
页码:286 / 295
页数:10
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