DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
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Ali, Jehad, Houbing Herbert Song, Vandana Sharma, and Mahmoud Ahmad Al-Khasawneh. “DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning.” IEEE Transactions on Consumer Electronics, 2024 . https://doi.org/10.1109/TCE.2024.3472707.
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Subjects
Machine learning
Internet of Things
Computer crime
Decision making
Denial-of-service attack
DDoS attacks
Performance evaluation
SDN
Tiny machine learning
Low power IoT
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Feature extraction
Accuracy
Inference algorithms
Training
Machine learning algorithms
Internet of Things
Computer crime
Decision making
Denial-of-service attack
DDoS attacks
Performance evaluation
SDN
Tiny machine learning
Low power IoT
UMBC Security and Optimization for Networked Globe Laboratory (SONG Lab)
Feature extraction
Accuracy
Inference algorithms
Training
Machine learning algorithms
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.