Intrusion Detection for IoT Environments Through Side-Channel and Machine Learning Techniques-Reference-Cited by-同舟云学术

Intrusion Detection for IoT Environments Through Side-Channel and Machine Learning Techniques

Published:2024 Issue: Volume:12 Page:98450-98465
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Campos Alejandro Domínguez¹^ORCID,Lemus-Prieto Felipe¹^ORCID,González-Sánchez José-Luis²^ORCID,Lindo Andrés Caro²^ORCID

Affiliation:

1. Extremadura Supercomputing, Technological Innovation and Research Center (CénitS), COMPUTAEX, Cáceres, Spain

2. Department of Computer and Telematics Systems Engineering, Universidad de Extremadura, Cáceres, Spain

Funder

Recovery and Resilience Facility, European Union-NextGenerationEU, through ‘‘Programa Investigo’’ of the Consejería de Educación y Empleo of the Junta de Extremadura

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10422967.pdf?arnumber=10422967

Reference37 articles.

1. That ‘Internet of Things’ thing;Ashton;RFID J.,2009

2. ML-IDS: A machine learning approach to detect wormhole attacks in Internet of Things

3. A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges

4. A Deep Learning Approach to Network Intrusion Detection

5. DeepBot: a time-based botnet detection with deep learning

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