Por favor, use este identificador para citar o enlazar este ítem: http://elartu.tntu.edu.ua/handle/lib/43375
Registro completo de metadatos
Campo DC Valor Lengua/Idioma
dc.contributor.advisorМатійчук, Любомир Павлович-
dc.contributor.authorБазан, Ірина Володимирівна-
dc.contributor.authorBazan, Iryna Volodymyrivna-
dc.date.accessioned2024-01-05T08:08:19Z-
dc.date.available2024-01-05T08:08:19Z-
dc.date.issued2023-12-26-
dc.date.submitted2023-12-12-
dc.identifier.citationБазан І.В. Методи інтелектуального аналізу даних для виявлення кіберзагроз у "розумних містах": кваліфікаційна робота освітнього рівня „Магістр“ „124 – системний аналіз“ / І.В. Базан. – Тернопіль : ТНТУ, 2023. – 85 с.uk_UA
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/43375-
dc.description.abstractКваліфікаційна робота присв’ячена розробці інтелектуальних методів аналізу даних для виявлення та протидії кіберзагрозам у «розумних містах». В першому розділі кваліфікаційної роботи описано архітектуру «розумного міста», висвітлено кіберзагрози для «розумного міста» В другому розділі кваліфікаційної роботи описано моделі взаємозалежності та оцінку ризиків загроз, досліджено методи виявлення атак, подано порівняльний опис показників порівняння та оцінки моделей. В третьому розділі кваліфікаційної роботи запропонована реалізація гібридної моделі виявлення кіберзагроз, протестовано квазіконкурентну нейронну мережу, подано відповідні набори даних та їх реалізованої їх обробку. В четвертому розділі кваліфікаційної роботи розглянуто забезпечення безпечної роботи з обладнанням. The qualification work is devoted to the development of intelligent data analysis methods for detecting and counteracting cyber threats in smart cities. The first section of the qualification work describes the architecture of the "smart city", highlights the cyber threats to the "smart city" The second section of the qualification work describes interdependence models and threat risk assessment, examines attack detection methods, and provides a comparative description of the indicators for comparing and evaluating models.The third section of the qualification work proposes the implementation of a hybrid model for detecting cyber threats, tests a quasi-competitive neural network, presents the relevant data sets and their implemented processing. The fourth section of the qualification work deals with ensuring safe operation of equipment.uk_UA
dc.description.tableofcontentsВСТУП 7 1 АНАЛІЗ ПРЕДМЕТНОЇ ОБЛАСТІ 9 1.1 Архітектура «розумного міста» 14 1.2 Кібер-загрози для «розумного міста» 17 1.2.1 Розвідувальні загрози 18 1.2.2 Диверсії інфраструктури 19 1.2.3 Маніпуляції з даними 21 1.2.4 Сторонні вразливості 24 1.3 Висновок до першого розділу 25 2 КАТЕГОРІЇ, МЕТОДИ ТА МОДЕЛІ ВИЯВЛЕННЯ КІБЕРЗАГРОЗ 27 2.1 Моделі взаємозалежності 27 2.2 Оцінка ризиків та розвідка загроз 30 2.3 Методи виявлення атак 33 2.4 Теоретичне підґрунтя 37 2.4.1 Машинне навчання та методи аналізу даних 37 2.4.2 Моделі на основі знань 39 2.5 Візуальний супровід 41 2.6 Вихідні дані: введення, інтерпретація та набори даних 42 2.7 Показники порівняння та оцінки моделей 44 2.7.1 Моделі взаємозалежності 44 2.7.2 Методи оцінки ризиків 44 2.7.3 Методи виявлення атак 46 2.8 Відкриті питання та виклики 50 2.9 Висновок до другого розділу 52 3 РЕАЛІЗАЦІЯ ЗАПРОПОНОВАНОЇ МОДЕЛІ 53 3.1 Згорткова нейронна мережа 53 3.2 Квазірекурентна нейронна мережа (QRNN) 54 3.3 Запропонована гібридна модель DL для кіберзагроз 55 3.4 Набори даних 56 3.5 Попередня обробка даних 58 3.6 Реалізація моделі 59 3.7 Інструменти та показники оцінювання 59 3.8 Оцінка та аналіз 60 3.9 Висновок до третього розділу 65 4 ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ 66 4.1 Питання щодо охорони праці 66 4.2 Питання щодо безпеки в надзвичайних ситуаціях 69 4.3 Висновок до четвертого розділу 72 ВИСНОВКИ 73 ПЕРЕЛІК ДЖЕРЕЛ 75 ДОДАТКИuk_UA
dc.language.isoukuk_UA
dc.subjectрозумне містоuk_UA
dc.subjectsmart cityuk_UA
dc.subjectбезпекаuk_UA
dc.subjectsecurityuk_UA
dc.subjectзагрозаuk_UA
dc.subjectthreatuk_UA
dc.subjectкібераналітикаuk_UA
dc.subjectcyber analyticsuk_UA
dc.subjectмашинне навчанняuk_UA
dc.subjectmachine learninguk_UA
dc.subjectглибоке навчанняuk_UA
dc.subjectdeep learninguk_UA
dc.subjectприйняття рішеньuk_UA
dc.subjectdecision-makinguk_UA
dc.titleМетоди інтелектуального аналізу даних для виявлення кіберзагроз у "розумних містах"uk_UA
dc.title.alternativeData mining methods for cyber threats detecting in "Smart cities"uk_UA
dc.typeMaster Thesisuk_UA
dc.rights.holder© Базан Ірина Володимирівна, 2023uk_UA
dc.contributor.committeeMemberОробчук, Олександра Романівна-
dc.coverage.placenameТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Українаuk_UA
dc.subject.udc004.056.53uk_UA
dc.relation.references1. United Nations. 68% of the world population projected to live in urban areas by 2050. 2018. https ://www.un.org. Дата доступу: 20.11.23.uk_UA
dc.relation.references2. City Profile. Smart cities world. https ://www.smart citiesworld.net. Дата доступу: 20.11.23.uk_UA
dc.relation.references3. Singapore uses IoT to create smart buildings. 2016. www.smart ‑energy.com Дата доступу: 20.11.23.uk_UA
dc.relation.references4. Building a smart + equitable city. The official website of the City of New York. 2015. f. Дата доступу: 20.11.23.uk_UA
dc.relation.references5. IBM. City of Rio de Janeiro and IBM collaborate to advance emergency response system; access to real‑time information empowers citizens. 2011. https ://www.prnew swire .com/ Дата доступу: 20.11.23.uk_UA
dc.relation.references6. Mclaughlin T. As shootings soar, Chicago police use technology to predict crime. 2017. https ://www.reute rs.com/ Дата доступу: 20.11.23.uk_UA
dc.relation.references7. The Register. Sweden ‘secretly blames’ hackers—not solar flares—for taking out air traffic control. The Register. 2018. https ://www.theregiste r.co.uk Дата доступу: 20.11.23.uk_UA
dc.relation.references8. Case DU. Analysis of the cyber attack on the Ukrainian power grid. Electricity Information Sharing and Analysis Center (E‑ISAC), vol. 388, 2016.uk_UA
dc.relation.references9. Kraszewski K. SamSam and the Silent Battle of Atlanta. In: 2019 11th international conference on cyber conflict (CyCon), 2019. vol. 900, p. 1–16.uk_UA
dc.relation.references10. Kan M. Ransomware strikes Baltimore’s 911 dispatch system. PCMag Asia. 2018. https ://sea.pcmag.com Дата доступу: 20.11.23.uk_UA
dc.relation.references11. Mettler K. Somebody keeps hacking these Dallas road signs with messages about Donald Trump Bernie Sanders and Harambe the gorilla. Washington, DC: WP Company; 2019.uk_UA
dc.relation.references78 Azmoodeh A, Dehghantanha A, Choo K‑KR. Robust malware detection for internet of (battlefield) things devices using deep Eigenspace learning. IEEE Trans Sustain Comput. 2018;4(1):88–95.uk_UA
dc.relation.references79 Dovom EM, Azmoodeh A, et al. Fuzzy pattern tree for edge malware detection and categorization in IoT. J Syst Archit. 2019;97:1–7.uk_UA
dc.relation.references80 Kumar A, Lim TJ. EDIMA: early detection of IoT malware network activity using machine learning techniques. In: 2019 IEEE 5th world forum on internet of things (WF‑IoT). 2019. p. 289–94. https ://doi.org/10.1109/WFIoT.2019.87671 94.uk_UA
dc.relation.references81 Meidan Y, et al. N‑baiot—network‑based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 2018;17(3):12–22.uk_UA
dc.relation.references82 Alazab VRM, et al A visualized botnet detection system based deep learning for the internet of things networks of smart cities. IEEE Trans Ind Appl. 2020.uk_UA
dc.relation.references83 Raza S, Wallgren L, Voigt T. SVELTE: real‑time intrusion detection in the internet of things. Ad Hoc Netw. 2013;11(8):2661–74.uk_UA
dc.relation.references84 Shreenivas D, Raza S, Voigt T. Intrusion detection in the RPL‑connected 6LoWPAN networks. In: Proceedings of the 3rd ACM international workshop on IoT privacy, trust, and security. 2017. p. 31–8.uk_UA
dc.relation.references85 Li D, Chen D, Goh J, Ng S. Anomaly detection with generative adversarial networks for multivariate time series. ArXiv Prepr. ArXiv180904758. 2018.uk_UA
dc.relation.references86 Azmoodeh A, et al. Detecting crypto‑ransomware in IoT networks based on energy consumption footprint. J Ambient Intell Humaniz Comput. 2018;9(4):1141–52.uk_UA
dc.relation.references87 Baracaldo N, Chen B, Ludwig H, Safavi A, Zhang R. Detecting poisoning attacks on machine learning in IoT environments. In: 2018 IEEE international congress on internet of things (ICIOT). 2018. p. 57–64.uk_UA
dc.relation.references101 UCSD network telescope—near‑real‑time network telescope dataset. www.caida.org. Дата доступу: 21.11.23.uk_UA
dc.relation.references88 Laishram R, Phoha VV. Curie: a method for protecting SVM classifier from poisoning attack. ArXiv Prepr. ArXiv160601584. 2016.uk_UA
dc.relation.references89 Goodfellow I, et al. Deep learning. Cambridge: MIT Press; 2016.uk_UA
dc.relation.references90 Hinton GE. Deep belief nets. 2010.uk_UA
dc.relation.references91 Senge R, Hüllermeier E. Fast fuzzy pattern tree learning for classification. IEEE Trans Fuzzy Syst. 2015;23(6):2024–33.uk_UA
dc.relation.references92 Goodfellow I, et al. Generative adversarial nets. In: Advances in neural information processing systems. Cambridge: MIT Press; 2014. p. 2672–80.uk_UA
dc.relation.references93 Edelkamp S, Schrödl S. Chapter 1—Introduction. In: Edelkamp S, Schrödl S, editors. Heuristic search. San Francisco: Morgan Kaufmann; 2012. p. 3–46.uk_UA
dc.relation.references94 Sanders WH, Meyer JF. Stochastic activity networks: formal definitions and concepts. In: School organized by the European Educational Forum. 2000. p. 315–43.uk_UA
dc.relation.references95 Chiola G, Dutheillet C, Franceschinis G, Haddad S. Stochastic well‑formed colored nets and symmetric modeling applications. IEEE Trans Comput. 1993;42(11):1343–60.uk_UA
dc.relation.references96 David HA, Moeschberger ML. The theory of competing risks. London: Charles Griffin and Company; 1978.uk_UA
dc.relation.references97 Dondossola G, Garrone G, Szanto J, Deconinck G, Loix T, Beitollahi H. ICT resilience of power control systems:uk_UA
dc.relation.references102 Rinaldi SM, et al. Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Syst Mag. 2001;21(6):11–25.uk_UA
dc.relation.references98 experimental results from the CRUTIAL testbeds. In: 2009 IEEE/IFIP international conference on dependable systems & networks. 2009. p. 554–9.uk_UA
dc.relation.references99 Goh J, et al. A dataset to support research in the design of secure water treatment systems. In: ICCIIS. 2016. p. 88–99.uk_UA
dc.relation.references100 Shodan. Доступно: http://shoda n.io. Дата доступу: 21.11.23.uk_UA
dc.relation.references103 Shameli‑Sendi A, Aghababaei‑Barzegar R, Cheriet M. Taxonomy of information security risk assessment (ISRA). Comput Secur. 2016;57:14–30.uk_UA
dc.relation.references104 Aven T, Heide B. Reliability and validity of risk analysis. Reliab Eng Syst Saf. 2009;94(11):1862–8.uk_UA
dc.relation.references105 Nurse JRC, Creese S, Roure DD. Security risk assessment in internet of things systems. IT Prof. 2017;19(5):20–6.uk_UA
dc.relation.references106 Xin Y, et al. Machine learning and deep learning methods for cybersecurity. IEEE;6:35365–81. 106. Thomas JJ, Cook KA. A visual analytics agenda. IEEE Comput Graph Appl. 2006;26(1):10–3.uk_UA
dc.relation.references107 Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.uk_UA
dc.relation.references108 M. Amine, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection : Approaches , datasets , and comparative study,” J. Inf. Secur. Appl., vol. 50, p. 102419, 2020.uk_UA
dc.relation.references109 N. Hasan, R. N. Toma, et al, “Electricity Theft Detection in Smart Grid Systems : A CNN-LSTM Based Approach,” Electr. Th. Detect. Smart Grid Syst. A CNN-LSTM Based Approach, vol. 12, no. 17, p. 3310, 2019.uk_UA
dc.relation.references110 D. Kwon, et al, “An Empirical Study on Network Anomaly Detection using Convolutional Neural Networks,” in In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), 2018, pp. 1595–1598.uk_UA
dc.relation.references12 Dallas warning sirens “set off by hacker”. BBC. 2017.uk_UA
dc.relation.references111 H. Liu, B. Lang, M. Liu, and H. Yan, “Knowledge-Based Systems CNN and RNN based payload classification methods for attack detection,” Knowledge-Based Syst., vol. 163, pp. 332–341, 2019.uk_UA
dc.relation.references112 F. Bolelli, L. Baraldi, F. Pollastri, and C. Grana, “A Hierarchical QuasiRecurrent approach to Video Captioning,” IEEE IPAS, 2018, pp. 162–167.uk_UA
dc.relation.references113 S. Merity, C. Xiong, and R. Socher, “Quasi-Recurrent Neural Network,” in arXiv, 2017, pp. 1–11.uk_UA
dc.relation.references114 M. Wang et al., “Quasi-fully Convolutional Neural Network with Variational Inference for Speech Synthesis,” in ICASSP 2019-2019 IEEE ICASSP, 2019, pp. 7060–7064.uk_UA
dc.relation.references115 J. Huang and Y. Feng, “Optimization of Recurrent Neural Networks on Natural Language Processing,” in Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition, 2019, pp. 39–45.uk_UA
dc.relation.references116 P. Wu, H. Guo, and N. Moustafa, “Pelican : A Deep Residual Network for Network Intrusion Detection,” arXiv, vol. 2001.08523, 2020.uk_UA
dc.relation.references117 D. Yao, et al, “Energy Theft Detection With Energy Privacy Preservation in the Smart Grid,” IEEE Internet Things J., vol. 6, no. 5, pp. 7659–7669, 2019.uk_UA
dc.relation.references118 Financial news of Ukraine (2018), “IT of Ukraine. Help can not be disturbed”. Доступно: https://news.finance.ua/ Дата доступу: 20.11.23.uk_UA
dc.relation.references119 Hi-Tech Business News (2018), “Trends in IT outsourcing in Ukraine”, Доступно: http://startupline.com.ua/ Дата доступу: 20.11.23.uk_UA
dc.relation.references120 Riskxchange (2022) «10 Effective IT Security Risk Assessment Tactics», Доступно: https://riskxchange.co/Дата доступу: 20.11.23.uk_UA
dc.relation.references13 Khan R, Kumar P, Jayakody DNK, Liyanage M. A survey on security and privacy of 5G technologies: potential solutions, recent advancements and future directions. IEEE Commun Surv Tutor. 2019;22(1):196–248.uk_UA
dc.relation.references121 ПНУ ім. Стефаника. Кафедра хімії. «Державна система моніторингу довкілля» Доступно: kc.pnu.edu.ua, Дата доступу: 20.11.23.uk_UA
dc.relation.references122 Стручок В.С. Безпека в надзвичайних ситуаціях. Методичний посібник для здобувачів освітнього ступеня «магістр» всіх спеціальностей денної та заочної (дистанційної) форм навчання / В.С.Стручок. — Тернопіль: ФОП Паляниця В. А., 2022. — 156 с.uk_UA
dc.relation.references123 Najla, A. T., Abbas, S. N., & Sujata, D. (2020). Cyber threat intelligence for secure smart city. arXiv preprint arXiv:2007.13233uk_UA
dc.relation.references14 Chan L, et al. Survey of AI in cybersecurity for information technology management. In: 2019 IEEE technology & engineering management conference (TEMSCON). 2019. p. 1–8.uk_UA
dc.relation.references15 Druzdzel MJ, Flynn RR. Decision support systems. In: Encyclopedia of library and information sciences. Boca Raton: CRC Press; 2017. p. 1200–8.uk_UA
dc.relation.references16 Ijaz S, Shah MA, Khan A, Ahmed M. Smart cities: a survey on security concerns. Int J Adv Comput Sci Appl. 2016;7(2):612–25.uk_UA
dc.relation.references17 Gharaibeh A, et al. Smart cities: a survey on data management, security, and enabling technologies. IEEE Commun Surv Tutor. 2017;19(4):2456–501.uk_UA
dc.relation.references18 Silva BN, Khan M, Han K. Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain Cities Soc. 2018;38:697–713.uk_UA
dc.relation.references19 Baig ZA, et al. Future challenges for smart cities: cyber‑security and digital forensics. Digit Investig. 2017;22:3–13.uk_UA
dc.relation.references20 Cui L, Xie G, Qu Y, Gao L, Yang Y. Security and privacy in smart cities: challenges and opportunities. IEEE;6:46134–45.uk_UA
dc.relation.references21 Sookhak M, Tang H, He Y, Yu FR. Security and privacy of smart cities: a survey, research issues and challenges. IEEE Commun Surv Tutor. 2019;21(2):1718–43. https ://doi.org/10.1109/COMST .2018.28672 88.uk_UA
dc.relation.references22 Talari S, Shafie‑Khah M, Siano P, Loia V, Tommasetti A, Catalão JP. A review of smart cities based on the internet of things concept. Energies. 2017;10(4):421.uk_UA
dc.relation.references23 Banerjee J, Das A, Sen A. A survey of interdependency models for critical infrastructure networks. ArXiv Prepr. ArXiv170205407. 2017.uk_UA
dc.relation.references24 Tøndel IA, Foros J, Kilskar SS, Hokstad P, Jaatun MG. Interdependencies and reliability in the combined ICT and power system: an overview of current research. Appl Comput Inform. 2018;14(1):17–27.uk_UA
dc.relation.references25 Kitchin R, Dodge M. The (in) security of smart cities: vulnerabilities, risks, mitigation, and prevention. J Urban Technol. 2019;26(2):47–65.uk_UA
dc.relation.references26 Vitunskaite M, He Y, Brandstetter T, Janicke H. Smart cities and cyber security: are we there yet? A comparative study on the role of standards, third party risk management and security ownership. Comput Secur. 2019;83:313–31.uk_UA
dc.relation.references27 Habibzadeh H, Nussbaum BH, Anjomshoa F, Kantarci B, Soyata T. A survey on cybersecurity, data privacy, and policy issues in cyber‑physical system deployments in smart cities. Sustain Cities Soc. 2019;50:101660.uk_UA
dc.relation.references28 Mehmood Y, Ahmad F, Yaqoob I, Adnane A, Imran M, Guizani S. Internet‑of‑things‑based smart cities: recent advances and challenges. IEEE Commun Mag. 2017;55(9):16–24. https ://doi.org/10.1109/MCOM.2017.16005 14.uk_UA
dc.relation.references29 Galluscio M, et al. A first empirical look on internet‑scale exploitations of IoT devices. In: 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC). 2017. p. 1–7.uk_UA
dc.relation.references30 Ercolani VJ, Patton MW, Chen H. Shodan visualized. In: 2016 IEEE conference on intelligence and security informatics (ISI). 2016. p. 193–5.uk_UA
dc.relation.references31 Patton M, Gross E, Chinn R, Forbis S, Walker L, Chen H. Uninvited connections: a study of vulnerable devices on the internet of things (IoT). In: 2014 IEEE joint intelligence and security informatics conference. 2014. p. 232–5.uk_UA
dc.relation.references32 PALO ALTO NETWORKS. Impacts of cyberattacks on IoT devices. www.sdxcentral.com Дата доступу: 20.11.23.uk_UA
dc.relation.references33 Sicato S, Costa J, Sharma PK, Loia V, Park JH. VPNFilter malware analysis on cyber threat in smart home network. Appl Sci. 2019;9(13):2763.uk_UA
dc.relation.references34 Zimba A, Wang Z, Mulenga M. Cryptojacking injection: a paradigm shift to cryptocurrency‑based web‑centric internet attacks. J Organ Comput Electron Commer. 2019;29(1):40–59.uk_UA
dc.relation.references35 Bou‑Harb E, Debbabi M, Assi C. A novel cyber security capability: inferring internet‑scale infections by correlating malware and probing activities. Comput Netw. 2016;94:327–43.uk_UA
dc.relation.references36 Bertino E, Islam N. Botnets and internet of things security. Computer. 2017;50(2):76–9.uk_UA
dc.relation.references37 Kumar M. DDoS attack takes down central heating system amidst winter in Finland. The Hacker News. 2016. https ://thehackern ews.com. Дата доступу: 20.11.23.uk_UA
dc.relation.references38 Trappe W, Howard R, Moore RS. Low‑energy security: limits and opportunities in the internet of things. IEEE Secur Priv. 2015;13(1):14–21.uk_UA
dc.relation.references39 Georgiou K, Xavier‑de‑Souza S, Eder K. The IoT energy challenge: a software perspective. IEEE Embed Syst Lett. 2017;10(3):53–6.uk_UA
dc.relation.references40 Mohurle S, Patil M. A brief study of wannacry threat: ransomware attack 2017. Int J Adv Res Comput Sci. 2017. https ://doi.org/10.26483 /IJARC S.V8I5.4021.uk_UA
dc.relation.references41 Ransomware attack on San Francisco public transit gives everyone a free ride. The Guardian. 2016. Доступно: www.thegu ardian.com Дата доступу: 20.11.23.uk_UA
dc.relation.references42 Liu Y, Ning P, Reiter MK. False data injection attacks against state estimation in electric power grids. ACM Trans Inf Syst Secur TISSEC. 2011;14(1):1–33.uk_UA
dc.relation.references43 Liang G, Zhao J, Luo F, Weller SR, Dong ZY. A review of false data injection attacks against modern power systems. IEEE Trans Smart Grid. 2016;8(4):1630–8.uk_UA
dc.relation.references44 Wurm J, Hoang K, Arias O, Sadeghi AR, Jin Y. Security analysis on consumer and industrial IoT devices. In: 2016 21st Asia and South Pacific design automation conference (ASP‑DAC). 2016. p. 519–24.uk_UA
dc.relation.references45 Van Zoonen L. Privacy concerns in smart cities. Gov Inf Q. 2016;33(3):472–80.uk_UA
dc.relation.references46 Usama M, Asim M, Latif S, Qadir J, et al. Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems. In: 2019 15th international wireless communications & mobile computing conference (IWCMC). 2019. p. 78–83.uk_UA
dc.relation.references47 Lin P, Swimmer M, Urano A, Hilt S, ve Vosseler R (2017) Securing smart cities moving toward utopia with security in mind. A TrendLabs Research Paper, Erişim Tarihi: 15 Eylül 2019.uk_UA
dc.relation.references48 Laugé A, Hernantes J, Sarriegi JM. Critical infrastructure dependencies: a holistic, dynamic and quantitative approach. Int J Crit Infrastruct Prot. 2015;8:16–23.uk_UA
dc.relation.references49 König S, Rass S. Investigating stochastic dependencies between critical infrastructures. Int J Adv Syst Meas. 2018;11:250–8.uk_UA
dc.relation.references50 Stergiopoulos G, Kotzanikolaou P, Theocharidou M, Gritzalis D. Risk mitigation strategies for critical infrastructures based on graph centrality analysis. Int J Crit Infrastruct Prot. 2015;10:34–44.uk_UA
dc.relation.references51 Stergiopoulos G, Kotzanikolaou P, Theocharidou M, Lykou G, Gritzalis D. Time‑based critical infrastructure dependency analysis for large‑scale and cross‑sectoral failures. Int J Crit Infrastruct Prot. 2016;12:46–60.uk_UA
dc.relation.references52 Beccuti M, Chiaradonna S, Di Giandomenico F, Donatelli S, Dondossola G, Franceschinis G. Quantification of dependencies between electrical and information infrastructures. Int J Crit Infrastruct Prot. 2012;5(1):14–27.uk_UA
dc.relation.references53 Bloomfield RE, Popov P, Salako K, Stankovic V, Wright D. Preliminary interdependency analysis: an approach to support critical‑infrastructure risk‑assessment. Reliab Eng Syst Saf. 2017;167:198–217.uk_UA
dc.relation.references54 Netkachov O, Popov P, Salako K. Quantification of the impact of cyber attack in critical infrastructures. In: International conference on computer safety, reliability, and security. 2014. p. 316–27.uk_UA
dc.relation.references55 Johansen C, Tien I. Probabilistic multi‑scale modeling of interdependencies between critical infrastructure systems for resilience. Sustain Resilient Infrastruct. 2018;3(1):1–15.uk_UA
dc.relation.references56 Heracleous C, Kolios P, Panayiotou CG, Ellinas G, Polycarpou MM. Hybrid systems modeling for critical infrastructures interdependency analysis. Reliab Eng Syst Saf. 2017;165:89–101.uk_UA
dc.relation.references57 Ferdowsi A, Saad W, Maham B, Mandayam NB. A Colonel Blotto game for interdependence‑aware cyber‑physical systems security in smart cities. In: Proceedings of the 2nd international workshop on science of smart city operations and platforms engineering. 2017. p. 7–12.uk_UA
dc.relation.references58 Li Z, Jin D, Hannon C, Shahidehpour M, Wang J. Assessing and mitigating cybersecurity risks of traffic light systems in smart cities. IET Cyber Phys Syst Theory Appl. 2016;1(1):60–9.uk_UA
dc.relation.references59 Kelarestaghi KB, Foruhandeh M, Heaslip K, Gerdes R. Intelligent transportation system security: impact‑oriented risk assessment of in‑vehicle networks. IEEE Intell Transp Syst Mag. 2019. https ://doi.org/10.1109/MITS.2018.28897 14.uk_UA
dc.relation.references60 Kotzanikolaou P, at all. Assessing n‑order dependencies between critical infrastructures. Int J Crit Infrastruct. 2013;9(1–2):93–110.uk_UA
dc.relation.references61 Neshenko N, Bou‑Harb E, Crichigno J, Kaddoum G, Ghani N. Demystifying IoT security: an exhaustive survey on IoT vulnerabilities and a first empirical look on internet‑scale IoT exploitations. IEEE Commun Surv Tutor. 2019;21(3):2702–33.uk_UA
dc.relation.references62 Sicari S, Rizzardi A, Miorandi D, Coen‑Porisini A. A risk assessment methodology for the internet of things. Comput Commun. 2018;129:67–79.uk_UA
dc.relation.references63 Wang H, Chen Z, Zhao J, Di X, Liu D. A vulnerability assessment method in industrial internet of things based on attack graph and maximum flow. 20;6:8599–609.uk_UA
dc.relation.references64 Mell P, Scarfone K, Romanosky S. Common vulnerability scoring system. IEEE Secur Priv. 2006;4(6):85–9.uk_UA
dc.relation.references65 Radanliev P, et al. Future developments in cyber risk assessment for the internet of things. Comput Ind. 2018;102:14–22.uk_UA
dc.relation.references66 Mohammad N. A multi‑tiered defense model for the security analysis of critical facilities in smart cities. 2019;7:152585–98.uk_UA
dc.relation.references67 Shivraj V, Rajan M, Balamuralidhar P. A graph theory based generic risk assessment framework for internet of things (IoT). In: 2017 IEEE international conference on ANTS. 2017. p. 1–6.uk_UA
dc.relation.references68 Mohsin M, Sardar MU, Hasan O, Anwar Z. IoTRiskAnalyzer: a probabilistic model checking based framework for formal risk analytics of the internet of things. IEEE;5:5494–505.uk_UA
dc.relation.references69 Falco G, et al. A master attack methodology for an AI‑based automated attack planner for smart cities. IEEE;6:48360–73.uk_UA
dc.relation.references70 Angelini M, Santucci G. Visual cyber situational awareness for critical infrastructures. In: 8th ISVICI. 2015. p. 83–92.uk_UA
dc.relation.references71 Wang P, Ali A, Kelly W. Data security and threat modeling for smart city infrastructure. In: 2015 international conference on cyber security of smart cities, industrial control system and communications (SSIC), 2015. p. 1–6.uk_UA
dc.relation.references72 Wang SP, Ledley RS. Computer architecture and security: fundamentals of designing secure computer systems. New York: Wiley; 2012.uk_UA
dc.relation.references73 Bou‑Harb E, Neshenko N. Cyber threat intelligence for the internet of things. New York: Springer; 2020.uk_UA
dc.relation.references74 Naik DR, Das LB, Bindiya TS. Wireless sensor networks with Zigbee and WiFi for environment monitoring, traffic management and vehicle monitoring in smart cities. In: 2018 IEEE 3rd international conference on computing, communication and security (ICCCS). 2018. p. 46–50.uk_UA
dc.relation.references75 Dowling S, Schukat M, Melvin H. A ZigBee honeypot to assess IoT cyberattack behavior. In: 2017 28th ISSC. 2017. p. 1–6.uk_UA
dc.relation.references76 Oza P, Foruhandeh M, Gerdes R, Chantem T. Secure traffic lights: replay attack detection for model‑based smart traffic controllers. In: Proceedings of the second ACM workshop on automotive and aerial vehicle security. 2020. p. 5–10.uk_UA
dc.relation.references77 He Y, Mendis GJ, Wei J. Real‑time detection of false data injection attacks in smart grid: a deep learning‑based intelligent mechanism. IEEE Trans Smart Grid. 2017;8(5):2505–16. https ://doi.org/10.1109/TSG.2017.27038 42.uk_UA
dc.contributor.affiliationТНТУ ім. І. Пулюя, Факультет комп’ютерно-інформаційних систем і програмної інженерії, Кафедра комп’ютерних наук, м. Тернопіль, Українаuk_UA
dc.coverage.countryUAuk_UA
Aparece en las colecciones: 124 — системний аналіз

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
Mag_2023_SAm_61_Bazan_I_V.pdf1,99 MBAdobe PDFVisualizar/Abrir


Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.

Herramientas de Administrador