Abstract-The threat that insiders pose to businesses, institutions and governmental organisations continues to be of serious concern. Recent industry surveys and academic literature provide unequivocal evidence to support the significance of this threat and its prevalence. Despite this, however, there is still no unifying framework to fully characterise insider attacks and to facilitate an understanding of the problem, its many components and how they all fit together. In this paper, we focus on this challenge and put forward a grounded framework for understanding and reflecting on the threat that insiders pose. Specifically, we propose a novel conceptualisation that is heavily grounded in insiderthreat case studies, existing literature and relevant psychological theory. The framework identifies several key elements within the problem space, concentrating not only on noteworthy events and indicators -technical and behavioural -of potential attacks, but also on attackers (e.g., the motivation behind malicious threats and the human factors related to unintentional ones), and on the range of attacks being witnessed. The real value of our framework is in its emphasis on bringing together and defining clearly the various aspects of insider threat, all based on realworld cases and pertinent literature. This can therefore act as a platform for general understanding of the threat, and also for reflection, modelling past attacks and looking for useful patterns.

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Abstract-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.

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