Abstract
IncidentResponseSim is a multi-agent-based simulation tool supporting risk management of online financial services, by performing a risk assessment of the quality of current countermeasures, in the light of the current and emerging threat environment. In this article, we present a set of simulations using incident response trees in combination with a quantitative model for estimating the direct economic consequences. The simulations generate expected fraud, and conditional fraud value at risk, given a specific fraud scenario. Additionally, we present how different trojan strategies result in different conditional fraud value at risk, given the underlying distribution of wealth in the online channel, and different levels of daily transaction limits. Furthermore, we show how these measures can be used together with return on security investment calculations to support decisions about future security investments.
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References
ENISA: Methodologies for the identification of critical infrastructure assets (2015)
ECB: Recommendations for the Security of Internet Payments (2013)
ENISA: ENISA Threat Landscape (2014)
FFIEC: Supplement to Authentication in an Internet Banking Environment (2011)
Schneier, B.: Secrets & Lies: Digital Security in a Networked World, pp. 318–333. John Wiley & Sons, New York (2000)
Mauw, S., Oostdijk, M.: Foundations of attack trees. In: Won, D.H., Kim, S. (eds.) ICISC 2005. LNCS, vol. 3935, pp. 186–198. Springer, Heidelberg (2006)
Edge, K.S., Dalton II, G.C., Raines, R.A., Mills, R.F.: Using attack and protection trees to analyze threats and defenses to homeland security. In: MILCOM. IEEE (2006)
Edge, K.S., Raines, R.A., Grimaila, M., Baldwin, R., Bennington, R., Reuter, C.: The use of protection trees to analyze security for an online banking system. In: The Proceedings of the 40th Hawaii International Conference on System Sciences (2006)
Kordy, B., Mauw, S., Radomirović, S., Schweitzer, P.: Foundations of attack–defense trees. In: Degano, P., Etalle, S., Guttman, J. (eds.) FAST 2010. LNCS, vol. 6561, pp. 80–95. Springer, Heidelberg (2011)
Pat-Cornell, M.E.: Fault trees vs. event trees in reliability analysis. Journal of Risk Analysis 4(3), 177–186 (1984)
Gorton, D.: Using Incident Response Trees as a Tool for Risk Management of Online Financial Services. Journal of Risk Analysis 34(9), 1763–1774 (2014)
Gorton, D.: Modeling fraud prevention of online services using incident response trees and value at risk. In: The Proceedings of the International Conference on Availability, Reliability and Security (ARES) (2015)
Wikipedia: Online banking. http://en.wikipedia.org/wiki/Online_banking (Accessed: August 30, 2015)
Julisch, K.: Risk-Based Payment Fraud Detection. Research Report, IBM Research, Zurich (2010)
Kaspersky: The Great Bank Robbery: Carbanak cybergang steals \({{{\$}}}1\text{bn}\) from 100 financial institutions worldwide. http://www.kaspersky.com/about/news/virus/2015/Carbanak-cybergang-steals-1-bn-USD-from-100-financial-institutions-worldwide (Accessed: August 30, 2015)
Florncio, D., Cormac, H.: Phishing and money mules. In: IEEE International Workshop on Information Forensics and Security, pp. 1–5 (2010)
Bank For International Settlements: An Explanatory Note on the Basel II IRB Risk Weight Function (2005)
Wikipedia: Agent-based Models. http://en.wikipedia.org/wiki/Agent-based_model (Accessed: August 30, 2015)
Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K., Balan, G.: MASON: A Multiagent Simulation Environment. Simulation, 517–527 (2005)
Lopez-Rojas, E.A., Gorton, D., Axelsson, S.: Using the RetSim Simulator for Fraud Detection Research. Int. Journal of Simulation and Process Modeling, 144–155 (2015)
Lopez-Rojas, E.A., Axelsson, S.: BankSim: a bank payment simulation for fraud detection research. In: The 26th European Modeling and Simulation Symposium (EMSS), pp. 144–152 (2014)
ENISA: Introduction to Return on Security Investment (2012)
PandaLabs: PandaLabs Annual Report 2012 Summary (2013)
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Gorton, D. (2015). IncidentResponseSim: An Agent-Based Simulation Tool for Risk Management of Online Fraud. In: Buchegger, S., Dam, M. (eds) Secure IT Systems. NordSec 2015. Lecture Notes in Computer Science, vol 9417. Springer, Cham. https://doi.org/10.1007/978-3-319-26502-5_12
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DOI: https://doi.org/10.1007/978-3-319-26502-5_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26501-8
Online ISBN: 978-3-319-26502-5
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