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DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
  • Ali, Jehad ;
  • Song, Houbing Herbert ;
  • Sharma, Vandana ;
  • Al-Khasawneh, Mahmoud Ahmad
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Publication Year
2024-01-01
Journal
IEEE Transactions on Consumer Electronics
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Transactions on Consumer Electronics
Keyword
DDoS attacksDecision makingLow power IoTMachine learningSDN
Mesh Keyword
Attack detectionDecisions makingsDenialof- service attacksDistributed denial of serviceDistributed denial of service attackLow PowerLow power internet of thingMachine learning algorithmsMachine-learningSoftware-defined networkings
All Science Classification Codes (ASJC)
Media TechnologyElectrical and Electronic Engineering
Abstract
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme.
ISSN
1558-4127
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/34516
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206344971&origin=inward
DOI
https://doi.org/10.1109/tce.2024.3472707
Journal URL
https://ieeexplore.ieee.org/servlet/opac?punumber=30
Type
Article
Funding
This work was supported partially by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education (NRF5199991514504)
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