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A review of Intrusion Detection Systems for the Internet of Things

  • Over the years, the Internet of Things has brought significant benefits to modern society, lives, and industries; however, the technology used has yet to mature sufficiently to provide secure devices and communication. Recently, the number of connected devices rapidly grows, thus adversaries have more opportunities to gain access to IoT devices and use them to launch what is called large-scaleOver the years, the Internet of Things has brought significant benefits to modern society, lives, and industries; however, the technology used has yet to mature sufficiently to provide secure devices and communication. Recently, the number of connected devices rapidly grows, thus adversaries have more opportunities to gain access to IoT devices and use them to launch what is called large-scale attacks. With the rapid proliferation of Internet of Things (IoT) devices, the need for efficient and effective Intrusion Detection System (IDS) tailored for IoT environments has become increasingly paramount. This paper explores various techniques employed in contemporary IoT IDS, including traditional signature-based approaches like Snort and Bro/Zeek, as well as emerging deep learning-based methods.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/9960
Bibliografische Angaben
Title (English):A review of Intrusion Detection Systems for the Internet of Things
Conference:International Conference on Emerging Technologies for Dependable Internet of Things (1. : 25-26 November 2024 : Sana'a, Yemen)
Author:Ammar Thabit Zahary, Nagi Ali Al-shaibany, Axel SikoraStaff MemberORCiDGND
Year of Publication:2024
Publisher:IEEE
First Page:1
Last Page:6
Parent Title (English):1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI 2024)
ISBN:979-8-3315-3355-7 (Elektronisch)
ISBN:979-8-3315-3356-4 (Print on Demand)
DOI:https://doi.org/10.1109/ICETI63946.2024.10777253
Language:English
Inhaltliche Informationen
Institutes:Forschung / ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik
Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Collections of the Offenburg University:Bibliografie
Tag:Intrusion Detection Systems; Learning systems; Measurement
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Konferenzbeitrag: h5-Index < 30
Open Access: Closed 
Licence (German):License LogoUrheberrechtlich geschützt