DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning

Title

DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning

Creator

Ali J.; Song H.H.; Sharma V.; Al-Khasawneh M.A.

Description

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. 1975-2011 IEEE.

Source

IEEE Transactions on Consumer Electronics

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

DDoS attacks; Decision making; Low power IoT; Machine learning; SDN

Coverage

Ali J., Ajou University, Department of Ai Convergence Network, Suwon, South Korea; Song H.H., Department of Information Systems, University of Maryland, Baltimore County (UMBC), 1000 Hilltop Circle, Baltimore, 21250, MD, United States; Sharma V., Christ University, India; Al-Khasawneh M.A., Skyline University College, University City Sharjah, School of Computing, Sharjah, 1797, United Arab Emirates, Applied Science Private University, Applied Science Research Center, Amman, Jordan, Jadara University Research Center, Jordan

Rights

Restricted Access

Relation

ISSN: 983063; CODEN: ITCED

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1109/TCE.2024.3472707

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206344971&doi=10.1109%2fTCE.2024.3472707&partnerID=40&md5=44d404dc7c55e8009a82ef4c770011b9