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Mirai Botnet Attack Detection in Low-Scale Network Traffic

Ebu Yusuf GÜVEN, Zeynep GÜRKAŞ-AYDIN*

Department of Computer Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey

* Corresponding Author: Zeynep GÜRKAŞ-AYDIN. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 419-437. https://doi.org/10.32604/iasc.2023.038043

Abstract

The Internet of Things (IoT) has aided in the development of new products and services. Due to the heterogeneity of IoT items and networks, traditional techniques cannot identify network risks. Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity. While the use of artificial intelligence eliminates the need to define rules, the training and retraining processes require additional processing power. This study proposes a methodology for analyzing constrained devices in IoT environments. We examined the relationship between different sized samples from the Kitsune dataset to simulate the Mirai attack on IoT devices. The training and retraining stages for the Mirai attack were also evaluated for accuracy. Various approaches are evaluated in smaller sample sizes to minimize training time on low-resource devices. Cross-validation was used to avoid overfitting classification methods during the learning process. We used the Bootstrapping technique to generate 1000, 10000, and 100000 samples to examine the performance metrics of different-sized variations of the dataset. In this study, we demonstrated that a sample size of 10000 is sufficient for 99,56% accuracy and learning in the detection of Mirai attacks in IoT devices.

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APA Style
GÜVEN, E.Y., GÜRKAŞ-AYDIN, Z. (2023). Mirai botnet attack detection in low-scale network traffic. Intelligent Automation & Soft Computing, 37(1), 419-437. https://doi.org/10.32604/iasc.2023.038043
Vancouver Style
GÜVEN EY, GÜRKAŞ-AYDIN Z. Mirai botnet attack detection in low-scale network traffic. Intell Automat Soft Comput . 2023;37(1):419-437 https://doi.org/10.32604/iasc.2023.038043
IEEE Style
E.Y. GÜVEN and Z. GÜRKAŞ-AYDIN, "Mirai Botnet Attack Detection in Low-Scale Network Traffic," Intell. Automat. Soft Comput. , vol. 37, no. 1, pp. 419-437. 2023. https://doi.org/10.32604/iasc.2023.038043



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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