In this article we illustrate the use of modeling and simulation early in the system life-cycle to improve security and reduce costs. The models that we develop for this illustration are inspired by problems in reliability analysis and supervisory control, for which similar models are used to quantify failure probabilities and rates. In the context of security, we propose that models of this general type can be used to understand trades between risk and cost while writing system requirements and during conceptual design, and thereby significantly reduce the need for expensive security corrections after a system enters operation.
GoertzelKWinogradT. Enhancing the development life cycle to produce secure software. Technical report DAN 358844, Data & Analysis Center for Software, Rome, NY, October2008.
2.
McGrawG. From the ground up: the DIMACS software security workshop. IEEE Secur Privacy2003; 1: 59–66.
3.
DiMaseDCollierZAHeffnerK. Systems engineering framework for cyber physical security and resilience. Environ Syst Decis2015: 1–10.
4.
OlamaMMNutaroJ. Secure it now or secure it later: the benefits of addressing cyber-security from the outset. Proc SPIE2013; 8757: 87570L.
5.
JaquithA. Security metrics: replacing fear, uncertainty, and doubt. Addison-Wesley, 2007.
6.
LeblancSPPartingtonAChapmanI. An overview of cyber attack and computer network operations simulation. In: Proceedings of the 2011 military modeling & simulation symposium (MMS’11), Boston, MA, 3–7 April 2011 pp. 92–100. Society for Computer Simulation International, 2011.
7.
KuhlMKistnerJCostantiniK. Cyber attack modeling and simulation for network security analysis. In: Proceedings of the 2007 winter simulation conference, Washington, DC, 9–12 December 2007, pp. 1180–1188.
8.
LeeuwenBVUriasVEldridgeJ. Cyber security analysis testbed: combining real, emulation, and simulation. In: Proceedings of the 2010 IEEE international Carnahan conference on security technology (ICCST), San Jose, CA, 5–8 October 2010, pp. 121–126.
9.
UriasVLeeuwenBVRichardsonB. Supervisory command and data acquisition (SCADA) system cyber security analysis using a live, virtual, and constructive (LVC) testbed. In: Proceedings of the military communications conference, 2012 (MILCOM 2012), Orlando, FL, 29 October − 1 November 2012, pp. 1–8.
10.
LinkovIEisenbergDAPlourdeK. Resilience metrics for cyber systems. Environ Syst Decis2013; 33: 471–476.
11.
NutaroJPattersonIAllgoodG. A method for engineering secure control systems with application to critical infrastructures. In: Proceedings of the 2012 international conference on security & management (SAM 2012), Las Vegas, NV, 16–19 July 2012, pp. 145–149.
12.
PattersonINutaroJAllgoodG. Optimizing investments in cyber-security for critical infrastructure. In: Proceedings of the eighth annual cyber security and information intelligence research workshop (CSIIRW’13), Oak Ridge, TN, 8–10 January 2013, pp. 20:1–20:4.
13.
CárdenasAAAminSLinZ-S. Attacks against process control systems: risk assessment, detection, and response. In: Proceedings of the 6th ACM symposium on information, computer and communications security (ASIACCS’11), 22–24 March, Hong King, China, pp. 355–366.
14.
AmariSMisraR. Closed-form expressions for distribution of sum of exponential random variables. IEEE Trans Reliab1997; 46: 519–522.
15.
SwainADGuttmannHE. Handbook of human reliability analysis with emphasis on nuclear power plant applications: final report. Technical Report NUREG/CR-1278, Sandia National Laboratories, August1983.
AlbertsDS. The agility advantage: a survival guide for complex enterprises and endeavors. Washington, DC: Department of Defense Command and Control Research Program, 2011.
18.
FikselJPolyviouMCroxtonKL. From risk to resilience: learning to deal with disruption. MIT Sloan Manage Rev2015; 56(2): 1–8.
19.
HinesDO. Simulation in support of flight testing. Technical Report RTO-AG-300 AC/323(SCI)TP/27 Volume 19, Research and Technology Organization, North Atlantic Treaty Organization, Neuilly-sur-Seine Cedex, France, September2000.
20.
LeversageDJamesE. Estimating a system’s mean time-to-compromise. IEEE Secur Privacy2008; 6: 52–60.
21.
SommestadTEkstedtMHolmH. The cyber security modeling language: a tool for assessing the vulnerability of enterprise system architectures. IEEE Syst J2013; 7: 363–373.
