Verifying the Application of Security Measures in IoT Software Systems with Model Learning

Salva, Sébastien; Blot, Elliott

doi:10.5220/0009872103500360

Cited by 4 publications

(16 citation statements)

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“…1. Integration of MLCA with some audit stages (in grey) [29] MLCA aims at assisting auditors to verify whether security properties hold on IoT systems with the automatic generation of behavioural models from event logs and with the generation of concrete security properties. Our approach can complement several existing security audit processes, e.g., the NIST or ETSI security audit frameworks.…”

Section: Mlca Overviewmentioning

confidence: 99%

“…This section details the MLCA's steps 2 to 5. More details of the model learning step are available in [30,29]. Before describing these steps, we provide some definitions and notations to be used throughout the remainder of the paper.…”

Section: The Model Learning Checking Approachmentioning

confidence: 99%

“…Despite the strong benefits brought by these approaches, the efforts required for understanding how an IoT system under audit (SUA) is structured and behaves or for generating security tests is yet tremendous. These observations motivated us to present in [29] an approach called MLCA combining model learning and model checking to assist auditors in the understanding of SUA by means of models, and in the verification of security properties on these models:…”

Section: Introductionmentioning

confidence: 99%

“…Contributions: this paper presents an extension of the MLCA algorithms given in [29], which mainly aims at improving both effectiveness and performance. We indeed showed that MLCA requires a manual inspection of the generated models to detect inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

“…This paper also provides an empirical evaluation, which investigates the sensitivity (ratio of true positives) and specificity (ratio of true negatives) of MLCA and its performance in terms of execution times. We also compare the MLCA of [29] with this new version. This empirical evaluation was carried out on event logs collected from 5 IoT systems.…”

Section: Introductionmentioning

confidence: 99%

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

Salva

Blot

2021

Communications in Computer and Information Science

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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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Section: Mlca Overviewmentioning

confidence: 99%

Section: The Model Learning Checking Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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