Towards a User and Role-based Sequential Behavioural Analysis Tool
for Insider Threat Detection

Ioannis Agrafiotis⁺, Philip Legg, Michael Goldsmith, and Sadie Creese

Cyber Security Centre, Department of Computer Science, University of Oxford, UK.

Abstract

Insider threat is recognised to be a significant problem and of great concern to both corporations and governments alike. Traditional intrusion detection systems are known to be ineffective due to the extensive knowledge and capability that insiders typically have regarding the organisational setup. Instead, more sophisticated measures are required to analyse the actions performed by those within the organisation, to assess whether their actions suggest that they pose a threat. In this paper, we propose a proof-of-concept that focuses on the use of activity trees to establish sequential-based analysis of employee behaviour. This concept combines the notions of previously-proposed techniques such as attack trees and behaviour trees. For a given employee, we define a tree that can represent all sequences of their observed behaviours. Over time, branches are either appended or created to reflect the new observations that are made on how the employee acts. We also incorporate a similarity measure to establish how different branches compare against each other. Attacks can be defined as where the similarity measure between a newly-observed branch and all existing branches is below a given acceptance criteria. The approach would allow an analyst to observe chains of events that result in low probability activities that could be deemed as unusual and therefore may be malicious. We demonstrate our proof-of-concept using third-party synthetic employee activity logs, to illustrate the practicalities of delivering this form of protective monitoring.

Keywords: Insider threat, Anomaly detection, Attack trees

+: Corresponding author: Ioannis Agrafiotis
Cyber Security Centre, Department of Computer Science, University of Oxford, Parks Road, Oxford,

OX1 3QD, UK, Tel: +44-(0)1865-273838, Email: ioannis.agrafiotis@cs.ox.ac.uk

Journal of Internet Services and Information Security (JISIS), 4(4): 127-137, November 2014 [pdf]