Characterising and predicting cyber attacks using the Cyber Attacker Model Profile (CAMP)

Purpose – Ethnographic studies of cyber attacks typically aim to explain a particular profile of attackers in qualitative terms. The purpose of this paper is to formalise some of the approaches to build a Cyber Attacker Model Profile (CAMP) that can be used to characterise and predict cyber attacks. Design/methodology/approach – The paper builds a model using social and economic independent or predictive variables from several eastern European countries and benchmarks indicators of cybercrime within the Australian financial services system. Findings – The paper found a very strong link between perceived corruption and GDP in two distinct groups of countries – corruption in Russia was closely linked to the GDP of Belarus, Moldova and Russia, while corruption in Lithuania was linked to GDP in Estonia, Latvia, Lithuania and Ukraine. At the same time corruption in Russia and Ukraine were also closely linked. These results support previous research that indicates a strong link between been legitimate economy and the black economy in many countries of Eastern Europe and the Baltic states. The results of the regression analysis suggest that a highly skilled workforce which is mobile and working in an environment of high perceived corruption in the target countries is related to increases in cybercrime even within Australia. It is important to note that the data used for the dependent and independent variables were gathered over a seven year time period, which included large economic shocks such as the global financial crisis. Originality/value – This is the first paper to use a modelling approach to directly show the relationship between various social, economic and demographic factors in the Baltic states and Eastern Europe, and the level of card skimming and card not present fraud in Australia. Acknowledgements: Paul A. Watters and Robert Layton are funded by IBM, Westpac, the State Government of Victoria and the Australian Federal Police