Using Deep Neural Network to Predict Botnet Attacks in IoT: A Comparative Study

Authors : Ladan, Nanbal Jibba; Katniyon, Henry David; Pam, Bulus Dung; Ramson, Emmanuel Nannim; Datti Useni Emmanuel; Mullah Sallau Nanlir

Volume/Issue : Volume 8 - 2023, Issue 2 - February

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The increased popularity being witnessed in the Internet of Things (IoT) domain has brought with it challenges in the area of security. From all indications, this growth we are witnessing will be of exponential proportions in the nearest future. The need to tackle security challenges is of utmost importance. This study was embarked upon to do exactly that. We were able to gain access to Bot-IoT dataset which was suitable since it was created specifically for IoT. A Deep Neural Network (DNN) was deployed and used to train and validate our dataset to predict and categorize the five types of botnet attacks present in the dataset. DNN was able to do that with an accuracy rate of 97%. Afterwards, a peer reviewed journal article which had used other Machine Learning (ML) models was selected and our results were compared. After the comparison, it was observed that RNN and LSTM had a slightly higher accuracy of 99% each but our model had a higher accuracy rate than SVM which stood at 88%.

Keywords : Internet of Things, Deep Learning, Machine Learning, Botnet.


Paper Submission Last Date
29 - February - 2024

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In 1-2 Days

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