The main goal of this work is to propose a model for the study and analysis of malware propagation in an industrial critical infrastructure. Specifically, an individual-based mathematical model is introduced and its parameters, variables and equations are explicitly defined and constructed. This model follows the agent based modeling paradigm which makes it extremely suitable for use within the small and heterogeneous environments where individual characteristics of all devices can be captured. The main strengths of this model are its ease of computational implementation (both in hardware and software), and its ability to simulate control measures (which can be of great help for security system administrators); however, this model strongly depends on a proper identification of the parameters.
AllenT.T., Introduction to discrete event simulation and agent-based modeling, Springer-Verlag, London, 2011.
2.
ArmandoA., MerloA. and VerderameL., Security considerations related to the use of mobile devices in the operation of critical infrastructures, Int J Crit Infrastruct Prot7 (2014), 247-256.
3.
AutyM., Anatomy of an advanced persistent threat, Netw Secur4 (2015), 13-16.
CaiX.T., HeF.Z., LiW.D., LiX.X. and WuY.Q., Encryption based partial sharing of CAD models, Integr Comput-Aided Eng22(3) (2015), 243-260.
6.
Cisco Systems. Cisco (2014), Annual Security Report. San Jose, CA; 2014. Available from http://www.cisco.com/web/ offer/gist_ty2_asset/Cisco_(2014)_ASR.pdf,
DadlaniA., KumarM.S., KimK. and SohrabyK., Stability and immunization analysis of a malware spread model over scale-free networks, IEEE Commun Lett18(11) (2014), 1907-1910.
9.
DasS.K., KantK. and ZhangN., Handbook on securing cyber-physical critical infrastructure, Morgan Kaufmann, Waltham, MA, 2012.
10.
DetkenK.O., FhomH.S., SethmannR. and DiederichG., Leveraging trusted network connect for secure connection of mobile devices to corporate networks, in: Communications: Wireless in Developing Countries and Networks of the Future, PontA., PujolleG. and RaghavanS.V., eds, IFIF Advances in Information and Communication Technology, Springer, Heidelberg 3272010, pp. 158-169.
EssaaidiM., GanzhaM. and PaprzyckiM., Software agents, agent systems and their applications, NATO science for peace and security series - D: information and communication security 32, IOS Press, 2012.
13.
GonzálezC.A. and HintonA., Detecting malicious software execution in programmable logic controllers using power fingerprint, in: Critical Infrastructure Protection VIII, ButtsJ. and ShenoiS., eds, IFIP Advances in Information and Communication Technology, Springer, Heidelberg 4412014, pp. 15-27.
14.
HagerottM., Stuxnet and the vital role of critical infrastructure operators and engineers, Int J Crit Infrastruct Prot7 (2014), 244-246.
15.
JohnsonT.A., Cybersecurity: Protecting critical infrastructures from cyber attack and cyber warfare, CRC Press, Boca Raton, FL, 2015.
16.
JorgensenS.E. and FathB.D., Individual-based models, Dev Env Model23 (2011), 291-308.
17.
KondakciS., A concise cost analysis of internet malware, Comput Secur28 (2009), 648-659.
18.
LiuG., ZhangJ. and ChenG., An approach to finding the cost-effective immunization targets for information assurance, Decis Support Syst67 (2014), 40-52.
19.
LuiijfE., Understanding cyber threats and vulnerabilities, in: Critical Information Infrastructure Protection, Lect Notes Comput Sci, LopezJ. et al., eds, Springer, Heidelberg, 2012, pp. 52-67.
20.
del ReyA.M., Mathematical modeling of the propagation of malware: A review, Secur Commun Netw8 (2015), 2561-2579.
21.
Martín del ReyA., Hernández EncinasA., Martín VaqueroJ., Queiruga DiosA. and Rodríguez SánchezG., A cellular automata model for mobile worm propagation, in: Bioinspired Computation in Artificial Intelligence, Lect Notes Comput Sci, AdeliH., Ferrández VicenteJ.M., Álvarez-SánchezJ.R., de la Paz LópezF. and Toledo-MoreoF.J., eds, Springer-Verlag, Heidelberg, 2015, pp. 107-116.
22.
Molina-GarcíaM., Calle-SánchezJ., González MerinoC., DuránA.F. and AlonsoJ.I., Design of in-bulding wireless networks deployments using evolutionary algorithms, Integr Comput-Aided Eng21(4) (2014), 367-385.
23.
Official website of the Department of Homeland Security [http://www.dhs.gov]. Washington, D.C., 2015.
24.
ParkA.J., TsangH.H., SunM. and GlässerU., An agent-based model and computational framework for counter-terrorism and public safety based on swarm intelligence, Secur Inf1 (2012), 23-31.
25.
PengS., ShuiY. and YangA., Smarphone malware and its propagation modeling: A survey, IEEE Commun Surv Tutor16(2) (2014), 925-941.
26.
PengS., WuM., WangG. and YuS., Propagation model of smartphone worms based on semi-markov process and social relationship graph, Comput Secur44 (2014), 92-103.
27.
PintoT., ValeZ., SousaT.M., PraçaI., SantosG. and MoraisH., Adaptive learning in agents behviour: A framework for electricity markets simulations, Integr Comput-Aided Eng21(4) (2014), 399-415.
28.
QuirósP., AlonsoP., DíazI. and MontesS., On the use of fuzzy partitions to protect data, Integr Comput-Aided Eng21(4) (2014), 355-366.
29.
RailsbackS.F. and GrimmV., Agent-based and individual-based modeling, Priceton University Press, Princeton, NJ, 2012.
30.
RamakrishnanS. and SrinivasanS., Intelligent agent based artificial immune system for computer security - a review, Artif Intell Re32 (2009), 13-43.
31.
ShakarianP., ShakarianJ. and RuefA., Duqu, Flame, Gauss, the next generation of cyber exploitation, in: Introduction to Cyber-Warfare, KatsaropoulosC. and RearickB., eds, A Multidisciplinary Approach, Elsevier, Waltham, MA, pp. 159-170.
32.
SchwennK., ColombiJ., WuT., OyamaK. and JohnsonA., Toward agent-based modeling of the U.S. department of defense acquisition system, Proc Comput Sci44 (2015), 383-392.
33.
Suarez-TangilG., TapiadorJ.E., Peris-LopezP. and RibagordaA., Evolution detection and analysis of malware in smart devices, IEEE Commun Surv Tutor16(2) (2014), 961-987.
34.
WarrenM.J. and LeitchS., Cyber security and protection of ICS systems: An Australian example, in: Cyber Security: Analytics, Technology and Automation, Intelligent Systems, Control and Automation: Science and Engineering, LehtoM., eds, Springer 782015, pp. 215-228.
35.
WenS., ZhouW., ZhangJ., XiangY., ZhouW.L., JiaW.J. et al., Modeling and analysis on the propagation dynamics of modern email malware, IEEE Trans Dependable Secur Comput11(4) (2014), 361-374.
36.
Journal editorial, Western energy firms come under concerted and successful cyber-attack, Netw Secur7 (2014), 1-2.
37.
WolframS., A new kind of science, Wolfram Media, Champaign, IL, 2002.
38.
WuC.H. and IrwinJ.D., Introduction to computer networks and cybersecurity, CRC Press, Boca Raton, FL, 2013.
39.
XuS.H., LuW.L., XuL. and ZhanZ.X., Adaptive epidemic dynamics in networks: Thresholds and control, ACM Trans Auton Adapt Syst8(4) (2014), Article number 19.
40.
YangL.X. and YangX., The impact of nonlinear infection rate on the spread of computer virus, Nonlinear Dyn8 (2015), 2561-2579.
