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Communication Dans Un Congrès Année : 2020

Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things

Résumé

The insecure growth of Internet-of-Things (IoT) can threaten its promising benefits to our daily life activities. Weak designs, low computational capabilities, and faulty protocol implementations are just a few examples that explain why IoT devices are nowadays highly prone to cyber-attacks. In this survey paper, we review approaches addressing this problem. We focus on machine learning-based solutions as a representative trend in the related literature. We survey and classify Machine Learning (ML)-based techniques that are suitable for the construction of Intrusion Detection Systems (IDS) for IoT. We contribute with a detailed classification of each approach based on our own taxonomy. Open issues and research challenges are also discussed and provided.
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Dates et versions

hal-03125767 , version 1 (29-01-2021)

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Citer

Omair Faraj, David Megias, Abdel-Mehsen Ahmad, Joaquin Garcia‐alfaro. Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things. ARES 2020: 15th international conference on Availability, Reliability and Security, Aug 2020, Dubin (online), Ireland. pp.79:1-79:10, ⟨10.1145/3407023.3407048⟩. ⟨hal-03125767⟩
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