Botnet attack detection using convolutional neural networks in the iot environment

Karaca K. N., ÇETİN A.

2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021, Kocaeli, Türkiye, 25 - 27 Ağustos 2021 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/inista52262.2021.9548445
  • Basıldığı Şehir: Kocaeli
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Botnet attack detection, CNN, Cybersecurity, Deep learning, IoT
  • Gazi Üniversitesi Adresli: Evet

Özet

© 2021 IEEE.In this study an experiment has been conducted on the detection of the most common botnet attacks in the Internet of Things (IoT) environment, using Convolutional Neural Networks (CNN) architecture. The developed CNN model was optimized by fluctuating the values of the hyperparameters at each repeating run. After the optimization process, the model was evaluated and obtained results were compared with the results in the literature. The entire dataset contains nine datasets of nine different IoT devices, each has ten types of botnet attacks. One of these datasets was used in the experiments. The proposed CNN model classified benign network traffic and botnet network traffic correctly, with a success rate (accuracy) of 97.98%. It performed better than Gaussian Naive Bayes (GNB), ANN, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) models which have success rates of 75.98%, 88.70%, 97.14%, and 97.48%, respectively.