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MLCA: A Model-Learning-Checking Approach for IoT Systems

Abstract : The Internet of Things (IoT) is a broad concept comprising a wide ecosystem of interconnected services and devices connected to the Internet. The IoT concept holds fabulous promises, but security aspects tend to be significant barriers for the adoption of large-scale IoT deployments. This paper proposes an approach to assist companies or organisations in the security audit of IoT systems. This approach called Model Learning and Checking Approach (MLCA) combines model learning for automatically extracting models from event logs, and model checking for verifying whether security properties, given under the form of generic LTL formulas hold on models. The originality of MLCA lies in the fact that auditors do not have to craft models or to be expert LTL users. The LTL formula instantiation, which makes security properties concrete, is indeed semi-automatically performed by means of an expert system composed of inference rules. The latter encode some expert knowledge, which can be applied again to the same kind of systems with less efforts. We evaluated MLCA on 5 IoT systems with security measures provided by the European ENISA institute. We show that MLCA is very effective in detecting security issues and provides results within reasonable time.
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https://hal.uca.fr/hal-03338612
Contributor : Sébastien Salva Connect in order to contact the contributor
Submitted on : Wednesday, September 8, 2021 - 6:47:13 PM
Last modification on : Monday, September 13, 2021 - 3:37:13 PM

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Sébastien Salva, Elliott Blot. MLCA: A Model-Learning-Checking Approach for IoT Systems. Communications in Computer and Information Science, vol. 1447, Software Technologies - 15th International Conference, 2020, Online Event, July 7-9, 2020, Revised Selected Papers, 1447, Springer, pp.70-97, 2021, Software Technologies, 978-3-030-83007-6/978-3-030-83006-9. ⟨10.1007/978-3-030-83007-6_4⟩. ⟨hal-03338612⟩

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