IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning

IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning

Botnet;IoT;Feature Engineering;SOFM;Machine Learning

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity,specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66seconds. The use of SOFM successfully reduced memory consumption from 3.08GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices

Susanto1*, Deris Stiawan2, Budi Santoso3, Alex Onesimus Sidabutar4,M. Agus Syamsul A5, Mohd. Yazid Idris6, Rahmat Budiarto7

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5871/995

Departmentof Informatica, Facultyof Engineering Science, Universitas Bina Insan, Lubuklinggau, Indonesia

28-12-2024

FAJAR BAGUS W

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