Machine Learning Models for Malicious Traffic Detection in IoT Networks /IoT-23 Dataset/-Reference-Cited by-同舟云学术

Machine Learning Models for Malicious Traffic Detection in IoT Networks /IoT-23 Dataset/

Published:2022 Issue: Volume: Page:69-84
ISSN:0302-9743
Container-title:Machine Learning for Networking
language:
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Author:

Oha Chibueze Victor,Farouk Fathima Shakoora,Patel Pujan Pankaj,Meka Prithvi,Nekkanti Sowmya,Nayini Bhageerath,Carvalho Smit Xavier,Desai Nisarg,Patel Manishkumar,Butakov Sergey

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-98978-1_5

Reference20 articles.

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3. Khraisat, A., Alazab, A.: A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity 4(18) (2021)

4. Beigi, E.B., Jazi, H.H., Stakhanova, N., Ghorbani, A.A.: Towards effective feature selection in machine learning-based botnet detection approaches. In: 2014 IEEE Conference on Communications and Network Security, San Francisco, CA, USA, pp. 247–255 (2014). https://doi.org/10.1109/CNS.2014.6997492

5. Jae-Gil, L., Jiawei, H., Xiaolei, L.: Trajectory outlier detection:a partition-and-detect framework. In: 2008 IEEE 24th International Conference on Data Engineering, Cancun, Mexico (2008)

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1. Understanding IoT-23 Dataset: A Benchmark for IoT Security Analysis;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

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3. BLoCNet: a hybrid, dataset-independent intrusion detection system using deep learning;International Journal of Information Security;2023-03-02