A model-driven approach for vulnerability evaluation of modern physical protection systems
Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a pr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Drago, Annarita [verfasserIn] Marrone, Stefano [verfasserIn] Mazzocca, Nicola [verfasserIn] Nardone, Roberto [verfasserIn] Tedesco, Annarita [verfasserIn] Vittorini, Valeria [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Software and systems modeling - Berlin : Springer, 2002, 18(2016), 1 vom: 26. Dez., Seite 523-556 |
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| Übergeordnetes Werk: |
volume:18 ; year:2016 ; number:1 ; day:26 ; month:12 ; pages:523-556 |
| Links: |
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| DOI / URN: |
10.1007/s10270-016-0572-7 |
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| Katalog-ID: |
SPR009315276 |
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| 520 | |a Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. | ||
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| 650 | 4 | |a Vulnerability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CIP_VAM UML profile |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bayesian Network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Model transformation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Railway infrastructure system |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Marrone, Stefano |e verfasserin |4 aut | |
| 700 | 1 | |a Mazzocca, Nicola |e verfasserin |4 aut | |
| 700 | 1 | |a Nardone, Roberto |e verfasserin |4 aut | |
| 700 | 1 | |a Tedesco, Annarita |e verfasserin |4 aut | |
| 700 | 1 | |a Vittorini, Valeria |e verfasserin |4 aut | |
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10.1007/s10270-016-0572-7 doi (DE-627)SPR009315276 (SPR)s10270-016-0572-7-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.50 bkl Drago, Annarita verfasserin aut A model-driven approach for vulnerability evaluation of modern physical protection systems 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. Physical security (dpeaa)DE-He213 Vulnerability (dpeaa)DE-He213 CIP_VAM UML profile (dpeaa)DE-He213 Bayesian Network (dpeaa)DE-He213 Model transformation (dpeaa)DE-He213 Railway infrastructure system (dpeaa)DE-He213 Marrone, Stefano verfasserin aut Mazzocca, Nicola verfasserin aut Nardone, Roberto verfasserin aut Tedesco, Annarita verfasserin aut Vittorini, Valeria verfasserin aut Enthalten in Software and systems modeling Berlin : Springer, 2002 18(2016), 1 vom: 26. Dez., Seite 523-556 (DE-627)355691426 (DE-600)2090971-8 1619-1374 nnns volume:18 year:2016 number:1 day:26 month:12 pages:523-556 https://dx.doi.org/10.1007/s10270-016-0572-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 18 2016 1 26 12 523-556 |
| spelling |
10.1007/s10270-016-0572-7 doi (DE-627)SPR009315276 (SPR)s10270-016-0572-7-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.50 bkl Drago, Annarita verfasserin aut A model-driven approach for vulnerability evaluation of modern physical protection systems 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. Physical security (dpeaa)DE-He213 Vulnerability (dpeaa)DE-He213 CIP_VAM UML profile (dpeaa)DE-He213 Bayesian Network (dpeaa)DE-He213 Model transformation (dpeaa)DE-He213 Railway infrastructure system (dpeaa)DE-He213 Marrone, Stefano verfasserin aut Mazzocca, Nicola verfasserin aut Nardone, Roberto verfasserin aut Tedesco, Annarita verfasserin aut Vittorini, Valeria verfasserin aut Enthalten in Software and systems modeling Berlin : Springer, 2002 18(2016), 1 vom: 26. Dez., Seite 523-556 (DE-627)355691426 (DE-600)2090971-8 1619-1374 nnns volume:18 year:2016 number:1 day:26 month:12 pages:523-556 https://dx.doi.org/10.1007/s10270-016-0572-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 18 2016 1 26 12 523-556 |
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10.1007/s10270-016-0572-7 doi (DE-627)SPR009315276 (SPR)s10270-016-0572-7-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.50 bkl Drago, Annarita verfasserin aut A model-driven approach for vulnerability evaluation of modern physical protection systems 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. Physical security (dpeaa)DE-He213 Vulnerability (dpeaa)DE-He213 CIP_VAM UML profile (dpeaa)DE-He213 Bayesian Network (dpeaa)DE-He213 Model transformation (dpeaa)DE-He213 Railway infrastructure system (dpeaa)DE-He213 Marrone, Stefano verfasserin aut Mazzocca, Nicola verfasserin aut Nardone, Roberto verfasserin aut Tedesco, Annarita verfasserin aut Vittorini, Valeria verfasserin aut Enthalten in Software and systems modeling Berlin : Springer, 2002 18(2016), 1 vom: 26. Dez., Seite 523-556 (DE-627)355691426 (DE-600)2090971-8 1619-1374 nnns volume:18 year:2016 number:1 day:26 month:12 pages:523-556 https://dx.doi.org/10.1007/s10270-016-0572-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 18 2016 1 26 12 523-556 |
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10.1007/s10270-016-0572-7 doi (DE-627)SPR009315276 (SPR)s10270-016-0572-7-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.50 bkl Drago, Annarita verfasserin aut A model-driven approach for vulnerability evaluation of modern physical protection systems 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. Physical security (dpeaa)DE-He213 Vulnerability (dpeaa)DE-He213 CIP_VAM UML profile (dpeaa)DE-He213 Bayesian Network (dpeaa)DE-He213 Model transformation (dpeaa)DE-He213 Railway infrastructure system (dpeaa)DE-He213 Marrone, Stefano verfasserin aut Mazzocca, Nicola verfasserin aut Nardone, Roberto verfasserin aut Tedesco, Annarita verfasserin aut Vittorini, Valeria verfasserin aut Enthalten in Software and systems modeling Berlin : Springer, 2002 18(2016), 1 vom: 26. Dez., Seite 523-556 (DE-627)355691426 (DE-600)2090971-8 1619-1374 nnns volume:18 year:2016 number:1 day:26 month:12 pages:523-556 https://dx.doi.org/10.1007/s10270-016-0572-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 18 2016 1 26 12 523-556 |
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10.1007/s10270-016-0572-7 doi (DE-627)SPR009315276 (SPR)s10270-016-0572-7-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.50 bkl Drago, Annarita verfasserin aut A model-driven approach for vulnerability evaluation of modern physical protection systems 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. Physical security (dpeaa)DE-He213 Vulnerability (dpeaa)DE-He213 CIP_VAM UML profile (dpeaa)DE-He213 Bayesian Network (dpeaa)DE-He213 Model transformation (dpeaa)DE-He213 Railway infrastructure system (dpeaa)DE-He213 Marrone, Stefano verfasserin aut Mazzocca, Nicola verfasserin aut Nardone, Roberto verfasserin aut Tedesco, Annarita verfasserin aut Vittorini, Valeria verfasserin aut Enthalten in Software and systems modeling Berlin : Springer, 2002 18(2016), 1 vom: 26. Dez., Seite 523-556 (DE-627)355691426 (DE-600)2090971-8 1619-1374 nnns volume:18 year:2016 number:1 day:26 month:12 pages:523-556 https://dx.doi.org/10.1007/s10270-016-0572-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE AR 18 2016 1 26 12 523-556 |
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Enthalten in Software and systems modeling 18(2016), 1 vom: 26. Dez., Seite 523-556 volume:18 year:2016 number:1 day:26 month:12 pages:523-556 |
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Enthalten in Software and systems modeling 18(2016), 1 vom: 26. Dez., Seite 523-556 volume:18 year:2016 number:1 day:26 month:12 pages:523-556 |
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Physical security Vulnerability CIP_VAM UML profile Bayesian Network Model transformation Railway infrastructure system |
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Software and systems modeling |
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Drago, Annarita @@aut@@ Marrone, Stefano @@aut@@ Mazzocca, Nicola @@aut@@ Nardone, Roberto @@aut@@ Tedesco, Annarita @@aut@@ Vittorini, Valeria @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR009315276</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110210655.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10270-016-0572-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR009315276</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10270-016-0572-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Drago, Annarita</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A model-driven approach for vulnerability evaluation of modern physical protection systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. 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Drago, Annarita |
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Drago, Annarita ddc 004 bkl 54.50 misc Physical security misc Vulnerability misc CIP_VAM UML profile misc Bayesian Network misc Model transformation misc Railway infrastructure system A model-driven approach for vulnerability evaluation of modern physical protection systems |
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004 ASE 54.50 bkl A model-driven approach for vulnerability evaluation of modern physical protection systems Physical security (dpeaa)DE-He213 Vulnerability (dpeaa)DE-He213 CIP_VAM UML profile (dpeaa)DE-He213 Bayesian Network (dpeaa)DE-He213 Model transformation (dpeaa)DE-He213 Railway infrastructure system (dpeaa)DE-He213 |
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model-driven approach for vulnerability evaluation of modern physical protection systems |
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A model-driven approach for vulnerability evaluation of modern physical protection systems |
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Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. |
| abstractGer |
Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. |
| abstract_unstemmed |
Abstract Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. |
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A model-driven approach for vulnerability evaluation of modern physical protection systems |
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Marrone, Stefano Mazzocca, Nicola Nardone, Roberto Tedesco, Annarita Vittorini, Valeria |
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| score |
7.3997297 |