A machine learning security framework for Iot systems


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Bagaa, M., Taleb, T., Bernabe, J. B. et Skarmeta, A. (2020). A machine learning security framework for Iot systems. IEEE Access, 8 . pp. 114066-114077. ISSN 2169-3536 DOI 10.1109/ACCESS.2020.2996214

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Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AI-based reaction agent that use ML-Models divided into network patterns analysis, along with anomaly-based intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomaly-based intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one-class SVM. The detection accuracy of anomalies achieved 99.71%. A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.

Type de document: Article
Mots-clés libres: Internet of Things Security Artificial intelligence SDN NFV Orchestration and MANO
Date de dépôt: 08 mai 2023 18:12
Dernière modification: 08 mai 2023 18:12
Version du document déposé: Version officielle de l'éditeur
URI: https://depot-e.uqtr.ca/id/eprint/10675

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