Automated Insider Threat Detection System Using User and Role-Based Profile Assessment

摘要

Organisations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorised access to sensitive organisational data are placed in a position of power that could well be abused and could cause significant damage to an organisation. This could range from financial theft and intellectual property theft, through to the destruction of property and business reputation. Traditional intrusion detection systems are not designed, nor are capable, of identifying those who act maliciously within an organisation. In this paper, we describe an automated system that is capable of detecting insider threats within an organisation. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role, and then use this to obtain a consistent representation of features that provide a rich description of the user's behaviour. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using 10 synthetic data-driven scenarios and found that the system can identify anomalous behaviour that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst.