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

Omair Faraj; David Megías; Abdel Mehsen Ahmad; Joaquin Garcia-Alfaro

doi:10.1145/3407023.3407048

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

Omair Faraj, David Megías, Abdel Mehsen Ahmad, Joaquin Garcia-Alfaro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES 2020
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450388337
DOIs	https://doi.org/10.1145/3407023.3407048
Publication status	Published - 25 Aug 2020
Event	15th International Conference on Availability, Reliability and Security, ARES 2020 - Virtual, Online, Ireland Duration: 25 Aug 2020 → 28 Aug 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	15th International Conference on Availability, Reliability and Security, ARES 2020
Country/Territory	Ireland
City	Virtual, Online
Period	25/08/20 → 28/08/20

Keywords

Internet of things (IoT)
Intrusion detection systems (IDS)
Machine learning
Network security

Access to Document

10.1145/3407023.3407048

Cite this

Faraj, O., Megías, D., Ahmad, A. M., & Garcia-Alfaro, J. (2020). Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things. In Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES 2020 Article 79 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3407023.3407048

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

abstract = "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.",

keywords = "Internet of things (IoT), Intrusion detection systems (IDS), Machine learning, Network security",

author = "Omair Faraj and David Meg{\'i}as and Ahmad, \{Abdel Mehsen\} and Joaquin Garcia-Alfaro",

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year = "2020",

month = aug,

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doi = "10.1145/3407023.3407048",

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Faraj, O, Megías, D, Ahmad, AM & Garcia-Alfaro, J 2020, Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things. in Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES 2020., 79, ACM International Conference Proceeding Series, Association for Computing Machinery, 15th International Conference on Availability, Reliability and Security, ARES 2020, Virtual, Online, Ireland, 25/08/20. https://doi.org/10.1145/3407023.3407048

Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things. / Faraj, Omair; Megías, David; Ahmad, Abdel Mehsen et al.
Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES 2020. Association for Computing Machinery, 2020. 79 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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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KW - Network security

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T3 - ACM International Conference Proceeding Series

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Faraj O, Megías D, Ahmad AM, Garcia-Alfaro J. Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things. In Proceedings of the 15th International Conference on Availability, Reliability and Security, ARES 2020. Association for Computing Machinery. 2020. 79. (ACM International Conference Proceeding Series). doi: 10.1145/3407023.3407048

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

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