- Guilherme Saraiva29,
- Filipe Apolinário ORCID:orcid.org/0000-0002-2067-323229 &
- Miguel L. Pardal ORCID:orcid.org/0000-0003-2872-730030
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14399))
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Abstract
In today’s interconnected world, robust cybersecurity measures are crucial, especially for Cyber-Physical Systems. While anomaly-based Intrusion Detection Systems can identify abnormal behaviors, interpreting the resulting alarms is challenging. An alternative approach utilizes invariant rules to describe system operations, providing clearer explanations for abnormal behaviors. In this context, invariant rules are conditions that must hold true for a system’s different operational modes. However, defining these rules is time-consuming and costly. This paper presents IM-DISCO, a tool that analyzes operational data to propose inference rules characterizing different modes of system operation. Deviations from these rules indicate anomalies, enabling continuous monitoring with incident detection and response. In our evaluation, focusing on rail transportation, we achieved 99.29% accuracy in detecting and characterizing operational modes using real-world train data. Additionally, we achieved 99.86% accuracy in identifying anomalies during simulated attacks. Notably, our results demonstrate an average detection time of 0.026 ms, enabling swift incident response to prevent catastrophic events.
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References
Adepu, S., Mathur, A.: From design to invariants: detecting attacks on cyber physical systems. In: 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 533–540. IEEE (2017)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Ahmed, C.M., et al.: NoisePrint: attack detection using sensor and process noise fingerprint in cyber physical systems. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security, pp. 483–497. ACM, Incheon ROK (2018)
Ahmed, C.M., Zhou, J., Mathur, A.P.: Noise matters: using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in CPS. In: Proceedings of the 34th Annual Computer Security Applications Conference, pp. 566–581. ACM, San Juan PR USA (2018)
Aliabadi, M.R., Kamath, A.A., Gascon-Samson, J., Pattabiraman, K.: Artinali: dynamic invariant detection for cyber-physical system security. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (2017)
Apolinário, F., Escravana, N., Hervé, É., Pardal, M.L., Correia, M.: Fingerci: generating specifications for critical infrastructures. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 183–186 (2022)
Apolinário, F., et al.: COMSEC: secure communications for baggage handling systems. In: Katsikas, S., et al. Computer Security. ESORICS 2022 International Workshops. ESORICS 2022. Lecture Notes in Computer Science, vol. 13785, pp. 329–345. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-25460-4_19
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pp. 255–264 (1997)
Carvalho, O., Apolinário, F., Escravana, N., Ribeiro, C.: CIIA: critical infrastructure impact assessment. In: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pp. 124–132 (2022)
Ceccato, M., Driouich, Y., Lucchese, M., Lanotte, R., Merro, M.: Towards reverse engineering of industrial physical processes. In: Katsikas, S., et al. Computer Security. ESORICS 2022 International Workshops. ESORICS 2022. Lecture Notes in Computer Science, vol. 13785. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-25460-4_15
Feng, C., Palleti, V., Mathur, A., Chana, D.: A systematic framework to generate invariants for anomaly detection in industrial control systems. In: NDSS (2019)
Feng, C., Tian, P.: Time series anomaly detection for cyber-physical systems via neural system identification and bayesian filtering. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD 2021, Association for Computing Machinery, New York, NY, USA (2021)
Fung, C., Srinarasi, S., Lucas, K., Phee, H.B., Bauer, L.: Perspectives from a comprehensive evaluation of reconstruction-based anomaly detection in industrial control systems. In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds.) Computer Security - ESORICS 2022, pp. 493–513. Springer Nature Switzerland, Cham (2022).https://doi.org/10.1007/978-3-031-17143-7_24
Grandini, M., Bagli, E., Visani, G.: Metrics for multi-class classification: an overview. arXiv preprintarXiv:2008.05756 (2020)
Hajj, S., El Sibai, R., Bou Abdo, J., Demerjian, J., Makhoul, A., Guyeux, C.: Anomaly-based intrusion detection systems: the requirements, methods, measurements, and datasets. Trans. Emerg. Telecommun. Technol.32(4), e4240 (2021)
Huang, Y.L., Cárdenas, A.A., Amin, S., Lin, Z.S., Tsai, H.Y., Sastry, S.: Understanding the physical and economic consequences of attacks on control systems. Int. J. Crit. Infrastruct. Prot.2(3), 73–83 (2009)
Kaouk, M., Flaus, J.M., Potet, M.L., Groz, R.: A review of intrusion detection systems for industrial control systems. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1699–1704 (2019)
Kiran, R.U., Reddy, P.K.: Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: Proceedings of the 14th International Conference on Extending Database Technology (2011)
Kumbhare, T.A., Chobe, S.V.: An overview of association rule mining algorithms. Int. J. Comput. Sci. Inf. Technol.5, 927–930 (2014)
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 (2008)
Lima, J., Apolinário, F., Escravana, N., Ribeiro, C.: BP-IDS: using business process specification to leverage intrusion detection in critical infrastructures. In: 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), pp. 7–12. IEEE (2020)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341 (1999)
Pal, K., Adepu, S., Goh, J.: Effectiveness of association rules mining for invariants generation in cyber-physical systems. In: 2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE), pp. 124–127. IEEE (2017)
Raschka, S.: MLxtend: providing machine learning and data science utilities and extensions to python’s scientific computing stack. J. Open Source Softw.3(24), 638 (2018)
Rubio, J.E., Alcaraz, C., Roman, R., Lopez, J.: Analysis of intrusion detection systems in industrial ecosystems. In: SECRYPT, pp. 116–128 (2017)
Umer, M.A., Mathur, A., Junejo, K.N., Adepu, S.: Generating invariants using design and data-centric approaches for distributed attack detection. Int. J. Crit. Infrastruct. Prot.28, 100341 (2020)
Wolsing, K., Thiemt, L., Sloun, C.v., Wagner, E., Wehrle, K., Henze, M.: Can industrial intrusion detection be simple? In: Atluri, V., Di Pietro, R., Jensen, C.D., Meng, W. (eds.) Computer Security - ESORICS 2022, vol. 13556, pp. 574–594. Springer Nature Switzerland, Cham (2022).https://doi.org/10.1007/978-3-031-17143-7_28
Yoong, C.H., Palleti, V.R., Maiti, R.R., Silva, A., Poskitt, C.M.: Deriving invariant checkers for critical infrastructure using axiomatic design principles. Cybersecurity4(1), 1–24 (2021)
Acknowledgement
Work supported by the European Commission through contract 101021797 (H2020 STARLIGHThttps://www.starlight-h2020.eu/).
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Authors and Affiliations
INOV INESC INOVAÇÃO, Lisbon, Portugal
Guilherme Saraiva & Filipe Apolinário
INESC-ID Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
Miguel L. Pardal
- Guilherme Saraiva
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- Filipe Apolinário
Search author on:PubMed Google Scholar
- Miguel L. Pardal
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Correspondence toFilipe Apolinário.
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Editors and Affiliations
Norwegian University of Science and Technology, Gjøvik, Norway
Sokratis Katsikas
Norwegian Computing Center, Oslo, Norway
Habtamu Abie
University of Trento, Trento, Italy
Silvio Ranise
University of Genoa, Genoa, Italy
Luca Verderame
Consiglio Nazionale delle Ricerche (CNR), Genoa, Italy
Enrico Cambiaso
SINTEF A.S., Oslo, Norway
Rita Ugarelli
Instituto Superior de Engenharia do Porto, Porto, Portugal
Isabel Praça
Hong Kong Polytechnic University, Hong Kong, China
Wenjuan Li
Technical University of Denmark, Kongens Lyngby, Denmark
Weizhi Meng
University of Nottingham, Nottingham, UK
Steven Furnell
Norwegian University of Science and Technology, Gjøvik, Norway
Basel Katt
Norwegian Computing Center, Oslo, Norway
Sandeep Pirbhulal
Institute for Energy Technology (IFE), Halden, Norway
Ankur Shukla
University of Calabria, Rende, Italy
Michele Ianni
University of Verona, Verona, Italy
Mila Dalla Preda
The University of Texas at San Antonio, San Antonio, TX, USA
Kim-Kwang Raymond Choo
University of Lisbon, Lisbon, Portugal
Miguel Pupo Correia
University of Twente, Enschede, The Netherlands
Abhishta Abhishta
University of Amsterdam, Amsterdam, The Netherlands
Giovanni Sileno
Open University in the Netherlands, Heerlen, The Netherlands
Mina Alishahi
Robert Gordon University, Aberdeen, UK
Harsha Kalutarage
Osaka University, Osaka, Japan
Naoto Yanai
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Saraiva, G., Apolinário, F., Pardal, M.L. (2024). IM-DISCO: Invariant Mining for Detecting IntrusionS in Critical Operations. In: Katsikas, S.,et al. Computer Security. ESORICS 2023 International Workshops. ESORICS 2023. Lecture Notes in Computer Science, vol 14399. Springer, Cham. https://doi.org/10.1007/978-3-031-54129-2_3
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