Machine Learning for Intrusion Detection: Stream Classification Guided by Clustering for Sustainable Security in IoT-Reference-Cited by-同舟云学术

Machine Learning for Intrusion Detection: Stream Classification Guided by Clustering for Sustainable Security in IoT

Published:2023-06-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Great Lakes Symposium on VLSI 2023
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Author:

Lopez Martin Manuel¹^ORCID,Shao Sicong¹^ORCID,Hariri Salim¹^ORCID,Salehi Soheil¹^ORCID

Affiliation:

1. University of Arizona, Tucson, AZ, USA

Funder

Korea Institute for Advancement of Technology (South Korea)

National Science Foundation

Department of Energy/National Nuclear Security Administration

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583781.3590271

Reference18 articles.

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2. Charan Bandi , Soheil Salehi , Rakibul Hassan , Sai Manoj P D , Houman Homayoun , and Setareh Rafatirad . 2021 . Ontology-Driven Framework for Trend Analysis of Vulnerabilities and Impacts in IoT Hardware. In 2021 IEEE 15th International Conference on Semantic Computing (ICSC). 211--214 . https://doi.org/10.1109/ICSC50631.2021.00045 10.1109/ICSC50631.2021.00045 Charan Bandi, Soheil Salehi, Rakibul Hassan, Sai Manoj P D, Houman Homayoun, and Setareh Rafatirad. 2021. Ontology-Driven Framework for Trend Analysis of Vulnerabilities and Impacts in IoT Hardware. In 2021 IEEE 15th International Conference on Semantic Computing (ICSC). 211--214. https://doi.org/10.1109/ICSC50631.2021.00045

3. ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets

4. A study of an incremental spectral meta-learner for nonstationary environments

5. Learning in Nonstationary Environments: A Survey

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