Abstract
Considering the massive increase in the number of crimes in the last decade, as well as the outlook toward smarter cities and more sustainable urban living, the emerging cyber-physical space (CPS) obtained by the interaction of such physical spaces with the cyber elements around them (e.g., think of Internet-of-Things devices or hyperconnected mobility), plays a key role in the protection of urban social living, e.g., social events or daily routines. For example, the hyperconnectedness of a CPS to many networks can lead to potential vulnerability. This vision paper aims to outline a vision and reference architecture where CPS protection is center-stage and where CPS models as well as so-called hybrid analytics work jointly to help the Law Enforcement Agents (LEAs), e.g., in event monitoring and early detection of criticalities. As a part of validating said reference architecture, we implement a case study in the scope of VISOR, a Dutch government project aimed at improving CPS protection using hybrid analytics. We conduct a field experiment in the Paaspop social event and festival grounds to test and select the most appropriate device configuration. There we experiment with a CPS protection pipeline featuring several components reflected in the reference architecture, e.g., the KGen middleware, a prototype tool to anonymize structured big data using genetic algorithms, and SENSEI, a framework for dark web marketplace analytics. We conclude that hybrid analytics offer a considerable ground for more sustainable CPS.
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References
Casale, G., Li, C.: Enhancing big data application design with the DICE framework. In: Mann, Z.Á., Stolz, V. (eds.) ESOCC 2017. CCIS, vol. 824, pp. 164–168. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-79090-9_13
Cervantes, H., Kazman, R.: Designing Software Architectures: A Practical Approach. Addison-Wesley Professional, Boston (2016)
Da Silva, T.L.C., de Macêdo, J.A., Casanova, M.A.: Discovering frequent mobility patterns on moving object data. In: Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, pp. 60–67 (2014)
De Pascale, D., Cascavilla, G., Sangiovanni, M., Tamburri, D.A., van den Heuvel, W.J.: Internet-of-things architectures for secure cyber-physical spaces: the visor experience report. arXiv preprint arXiv:2204.01531 (2022)
De Pascale, D., Cascavilla, G., Tamburri, D.A., Van Den Heuvel, W.J.: Sensei: scraper for enhanced analysis to evaluate illicit trends. SSRN 3976047 (2022)
Du, B., Liu, C., Zhou, W., Hou, Z., Xiong, H.: Detecting pickpocket suspects from large-scale public transit records. IEEE Trans. Knowl. Data Eng. 31(3), 465–478 (2018)
El Emam, K., et al.: A globally optimal k-anonymity method for the de-identification of health data. J. Am. Med. Inform. Assoc. 16(5), 670–682 (2009)
Garroppo, R.G., Niccolini, S.: Anomaly detection mechanisms to find social events using cellular traffic data. Comput. Commun. 116, 240–252 (2018)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)
Hayes, D.R., Cappa, F., Cardon, J.: A framework for more effective dark web marketplace investigations. Information 9(8), 186 (2018)
Hogan, A., et al.: Knowledge graphs (2020)
Lee, E.A.: Cyber physical systems: design challenges. In: 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369. IEEE (2008)
Lee, E.A.: CPS foundations. In: Design Automation Conference, pp. 737–742. IEEE (2010)
Lee, E.A.: The past, present and future of cyber-physical systems: a focus on models. Sensors 15(3), 4837–4869 (2015)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 49–60. ACM (2005)
Nagarajan, S.M., Deverajan, G.G., Bashir, A.K., Mahapatra, R.P., Al-Numay, M.S.: IADF-CPS: intelligent anomaly detection framework towards cyber physical systems. Comput. Commun. (2022)
Perrone, G., Vecchio, M., Pecori, R., Giaffreda, R., et al.: The day after Mirai: a survey on MQTT security solutions after the largest cyber-attack carried out through an army of IoT devices. In: IoTBDS, pp. 246–253 (2017)
Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)
Sweeney, L.: Guaranteeing anonymity when sharing medical data, the Datafly system. In: Proceedings of the AMIA Annual Fall Symposium, p. 51. American Medical Informatics Association (1997)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. Internat. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(05), 571–588 (2002)
Voigt, P., von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st edn. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7
Wikipedia: The Hidden Wiki (2022). https://en.wikipedia.org/wiki/The_Hidden_Wiki
Yuan, Y., Fang, J., Wang, Q.: Online anomaly detection in crowd scenes via structure analysis. IEEE Trans. Cybern. 45(3), 548–561 (2014)
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De Pascale, D., Sangiovanni, M., Cascavilla, G., Tamburri, D.A., Van Den Heuvel, WJ. (2023). Securing Cyber-Physical Spaces with Hybrid Analytics: Vision and Reference Architecture. In: Katsikas, S., et al. Computer Security. ESORICS 2022 International Workshops. ESORICS 2022. Lecture Notes in Computer Science, vol 13785. Springer, Cham. https://doi.org/10.1007/978-3-031-25460-4_23
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