Novel Machine Learning (ML) Algorithms to Classify IPv6 Network Traffic in Resource-Limited Systems

Yıl 2022, , 219 - 224, 10.10.2022

Yıldıran Yılmaz , Selim Buyrukoğlu , Muzaffer Alım

https://doi.org/10.53070/bbd.1172706

Öz

Providing machine learning (ML) based security in heterogeneous IoT networks including resource-constrained devices is a challenge because of the fact that conventional ML algorithms require heavy computations. Therefore, in this paper, lightweight ProtoNN, CMSIS-NN, and Bonsai tree ML algorithms were evaluated by using performance metrics such as testing accuracy, precision, F1 score and recall to test their classification ability on the IPv6 network dataset generated on resource-scarce embedded devices. The Bonsai tree algorithm provided the best performance results in all metrics (98.8 in accuracy, 98.9% in F1 score, 99.2% in precision, and 98.8% in recall) compared to the ProtoNN, and CMSIS-NN algorithms.

Anahtar Kelimeler

Embedded systems, machine learning, lightweight ML algorithms, IPv6 Network, cyber attack

Destekleyen Kurum

Recep Tayyip Erdogan University

Kaynakça

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