Algorithms and Methods for Organised Cybercrime Analysis
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Law enforcement agencies reports that Organised Criminal Groups are moving more of their activities from traditional crime into the cyber domain. Where they form so-called Darknets, whose purpose is to act as marketplaces for illegal material, products, and services. These activities form a part of the Crime-as-a- Service business model, which drives the digital underground economy by providing services that facilitate almost any type of cybercrime. The challenge for law enforcement is knowing which entity to target for effectively taking down these network structures. This thesis seeks to use graph-based algorithms and methods to analyse network structures to identify interesting and relevant individuals within such networks. More specifically, it proposes Social Network Analysis (SNA) methods for the process of identification of such individuals. The thesis analyse each SNA method to identify those methods that identify the most central individuals within a network. Also, it will analyse how the usage of different graph construction techniques can be applied to the process of identification. The thesis contributions is to try to bridge the gap between law enforcement agencies and cybercrimials by proposing an improved way of prioritising individuals within these networks.