Development of an Malicious Insider Composite Vulnerability Assessment Methodology THESIS

Trusted employees pose a major threat to information systems. Despite advances in prevention, detection, and response techniques, the number of malicious insider incidents and their associated costs have yet to decline. There are very few vulnerability and impact models capable of providing information owners with the ability to comprehensively assess the effectiveness an organization’s malicious insider mitigation strategies. This research uses a multi-dimensional approach: content analysis, attack tree framework, and an intent driven taxonomy model are used to develop a malicious insider Decision Support System (DSS) tool. The tool’s output provides an assessment of a malicious insider’s composite vulnerability levels based upon aggregated vulnerability assessment and impact assessment levels. The DSS tool’s utility and applicability is demonstrated using a notional example. This research gives information owners data to more appropriately allocate scarce security resources.