Characterization of IEEE 802.11 communications and detection of low power jamming attacks in noncontrolled environment based on a clustering study

Autor: Jonathan Villain, Christophe Gransart, Eric Pierre Simon, Virginie Deniau, Anthony Fleury
Přispěvatelé: Laboratoire Électronique Ondes et Signaux pour les Transports (COSYS-LEOST ), Université Gustave Eiffel, Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), Institut Mines-Télécom [Paris] (IMT), Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN (TELICE - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), SECOURT, This work was supported by the ELSAT2020 project which is co-financed by the European Union with the European Regional DevelopmentFund, the French state and the Hauts de France Region Council., European Project, Université de Lille-Université Gustave Eiffel, Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: IEEE Systems Journal
IEEE Systems Journal, 2022, 16 (1), pp 683-692. ⟨10.1109/JSYST.2020.3045365⟩
IEEE Systems Journal, In press, pp.1-10. ⟨10.1109/JSYST.2020.3045365⟩
ISSN: 1937-9234
1932-8184
DOI: 10.1109/JSYST.2020.3045365⟩
Popis: International audience; Wireless connections are more and more used in different applications and in public areas for services to consumers but also for handling (sometimes) sensitive communications (for instance in railway systems or for remote video monitoring systems). Such systems can have to face different kind of attacks that target the behind service. Our work aims to detect, as soon as possible and online, attacks that can occur on wireless networks, to be able to react very quickly. In this paper, we present some results of data analysis methods, on Wi-Fi signals, to differentiate the ones with attacks from the ones without. This study focuses on low power jamming attacks with a slight or even no impact on Wi-Fi communications. This is more challenging than detecting high power jamming attacks which have already been addressed in the literature. Being able to detect a low impact attack is a crucial issue in a global security strategy, making it possible to launch countermeasures before the interruption of the communication. The Wi-Fi bands are also in the ISM frequencies, making the environment complicated to analyze. Clustering methods such as Agglomerative Hierarchical Clustering are used to identify some clusters and then to map them to the real classes (with or without attacks). A deep analysis of the clusters obtained in a dataset acquired in uncontrolled conditions is carried out. This is done in order to understand what is responsible of the clustering assignment of the different points and to extract the clusters which can be used to design a detection attack strategy.
Databáze: OpenAIRE
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