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Optimisation of security system design by quantitative risk assessment and genetic algorithms
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-2833-7196
2011 (English)In: International Journal of Risk Assessment and Management, ISSN 1466-8297, E-ISSN 1741-5241, Vol. 15, no 2-3, p. 205-221Article in journal (Refereed) Published
Abstract [en]

The design of physical security systems for critical infrastructures is a delicate task that requires a balance between the cost of protection mechanisms and their expected effect on risk mitigation. This paper presents an approach usable to support the design of security systems by automatically optimising some parameters, basing on external constraints (e.g., limited available budget) and using quantitative risk assessment. Risk assessment is performed using a software tool that implements a quantitative methodology. The methodology accounts for the attributes of threats (frequency, system vulnerability, expected consequences) and protection mechanisms (cost, effectiveness, coverage, etc.). The optimisation is performed by means of genetic algorithms with the objective of achieving the set of parameters that minimises the risk while fitting external budget constraints, hence maximising the return on investment. The paper also describes an example application of the approach to the design of physical security systems for metro railways. © 2011 Inderscience Enterprises Ltd.

Place, publisher, year, edition, pages
2011. Vol. 15, no 2-3, p. 205-221
Keywords [en]
Decision support systems, Infrastructure security, Rail-based mass transit systems, Return on investment, Risk assessment
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:lnu:diva-73679DOI: 10.1504/IJRAM.2011.042117Scopus ID: 2-s2.0-80052198745OAI: oai:DiVA.org:lnu-73679DiVA, id: diva2:1213901
Note

Cited By :4; Export Date: 26 April 2018; Article; Correspondence Address: Flammini, F.; Innovation and Competitiveness Unit, ANSALDO STS Italy, Via Argine 425, Napoli, Italy; email: francesco.flammini@ansaldo-sts.com; References: Abraham, A., Grosan, C., Snasel, V., Programming risk assessment models for online security evaluation systems (2009) UKSim 2009, 11th International Conference on Computer Modelling and Simulation, pp. 41-46; Alotto, P.G., Stochastic algorithms in electromagnetic optimization' (1998) IEEE Transactions on Magnetics, 34 (5), pp. 3674-3684; Bang, Y.H., The design and development for risk analysis automatic tool in lecture notes in computer science (2004) 3043/2004, Computational Science and Its Applications -Proc. ICCSA 2004, , Springer; Banković, Z., Stepanović, D., Bojanić, S., Nieto-Taladriz, O., Improving network security using genetic algorithm approach (2007) Computers & Electrical Engineering, 33 (5-6), pp. 438-451; Broder, J.F., (2006) Risk Analysis and the Security Survey Butterworth-Heinemann; Chambers, L.D., (2000) The Practical Handbook of Genetic Algorithms: Applications, 2nd Ed., , CRC Press; Ferentinos, K.P., Tsiligiridis, T.A., Adaptive design optimization of wireless sensor networks using genetic algorithms (2007) Computer Networks, 51 (4), pp. 1031-1051. , DOI 10.1016/j.comnet.2006.06.013, PII S1389128606001678; Flammini, F., Wireless sensor data fusion for critical infrastructure security in advances in intelligent and soft computing (2009) Proc. International Workshop on Computational Intelligence in Security for Information Systems, CISIS'08, 53, pp. 92-99. , Springer; Flammini, F., Gaglione, A., Mazzocca, N., Pragliola, C., Quantitative security risk assessment and management for railway transportation infrastructures (2008) Proc. 3rd International Workshop on Critical Information Infrastructures Security, pp. 213-223. , CRITIS'08, 13-15 October Springer, Frascati Rome, Italy; Garcia, M.L., (2001) The Design and Evaluation of Physical Protection Systems, , Butterworth-Heinemann; Garcia, M.L., (2005) Vulnerability Assessment of Physical Protection Systems, , Butterworth-Heinemann; Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , Addison-Wesley, Reading, MA; Hansen, J.V., Lowry, P.B., Meservy, R.D., McDonald, D.M., Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection (2007) Decision Support Systems, 43 (4), pp. 1362-1374. , DOI 10.1016/j.dss.2006.04.004, PII S0167923606000625; Holland, J.H., (1975) Adaption in Natural and Artificial Systems, , University of Michigan Press, Ann Arbor; Holland, J.H., Genetic algorithms (1992) Scientific American; Indu, S., Chaudhury, S., Mittal, N.R., Bhattacharyya, A., Optimal sensor placement for surveillance of large spaces (2009) Third ACM/IEEE International Conference on Distributed Smart Cameras, 2009, pp. 1-8. , ICDSC 2009, Como, IT, 30 August to 2 September; Martins, F.V.C., An evolutionary dynamic approach for designing wireless sensor networks for real time monitoring (2010) 2010 IEEE/ACM 14th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 161-168. , 17-20 October ,Fairfax, VA; Nicol, D.M., Sanders, W.H., Trivedi, K.S., Model-based evaluation: From dependability to security' (2004) IEEE Transactions on Dependable and Secure Computing, 1 (1), pp. 48-65; Painton, L., Campbell, J., Genetic algorithms in optimization of system reliability' (2005) IEEE Transactions on Reliability, 44 (2), pp. 172-178; (2004) Transit Security Design Considerations, Federal Transit Administration, , US Department of Transportation Final Report; Whitley, D., A genetic algorithm tutorial (1994) Statistics and Computing, 4 (2), pp. 65-85; Wilson, J.M., (2008) Securing America's Passenger-Rail Systems, , RAND Corporation; Yao, X.H., A network intrusion detection approach combined with genetic algorithm and back propagation neural network (2010) 2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies (EDT), pp. 402-405. , 17-18 April, Shenzen, CN

Available from: 2018-06-05 Created: 2018-06-05 Last updated: 2018-06-05

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