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AuthorIlango, Harun Surej
AuthorMa, Maode
AuthorSu, Rong
Available date2024-07-24T10:13:52Z
Publication Date2021
Publication NameProceedings - IEEE Congress on Cybermatics: 2021 IEEE International Conferences on Internet of Things, iThings 2021, IEEE Green Computing and Communications, GreenCom 2021, IEEE Cyber, Physical and Social Computing, CPSCom 2021 and IEEE Smart Data, SmartData 2021
ResourceScopus
URIhttp://dx.doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00031
URIhttp://hdl.handle.net/10576/57075
AbstractThe lack of standardization and the heterogeneous nature of IoT, exacerbated the issue of security and privacy. In recent literature, to improve security at the network level, the possibility of using SDN for IoT networks was explored. An LR DoS attack is an insidious DoS attack that hinders the availability of the network to its legitimate users. LR DoS attacks are difficult to detect and can be deadly to a network due to their hidden nature. Recently, the possibility of using ML or DL algorithms to detect LR DoS attacks have gained traction due to advancements in computing technology. The ML and DL algorithms that are currently available in the literature have a detection rate of 95 percent at best. In this work, a novel deep learning scheme called FFCNN is proposed to detect LR DoS attacks in a SDN environment. The CIC DoS 2017 and CIC IDS 2017 datasets provided by the Canadian Institute of Cybersecurity were used for the experimental analysis. The empirical analysis of the proposed algorithm shows that it outperforms the existing machine learning based algorithms. FFCNN promises a lower false alarm rate and better detection rate in the detection of LR DoS.
SponsorACKNOWLEDGMENT This research is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre Positioning (IAF-PP) (Award A19D6a0053). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of A*STAR.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCIC DoS 2017
CIC IDS 2017
Deep Learning
Internet of Things
Low-Rate DoS Attacks
Network Security
Software Defined Networking
TitleLow Rate DoS Attack Detection in IoT - SDN using Deep Learning
TypeConference
Pagination115-120
dc.accessType Abstract Only


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