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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Pohan, Mara Muda
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Computer Science & IT Decision Sciences, Operations Research & Management

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Injection Attack Detection on Internet of Things Device with Machine Learning Method Pohan, Mara Muda; Soewito, Benfano
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 8, No 1 (2023): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v8i1.556

Abstract

The Internet of Things (IoT) Industry is growing rapidly, security surrounding this Industry has to be upgraded. This study analyzes which machine learning performs the best in detecting Injection Attacks in IoT devices. The proposed machine learning methods includes Catboost, Decision Tree, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). This study uses Edge-IIoTset dataset. The traffic data obtained in this dataset comes from 13 different types of IoT devices which contains 10 files with normal traffic and 14 files of attack traffics. This study takes normal traffic and injection attacks traffic from Edge-IIoTset. Results shows that Catboost machine learning model performs the best in terms of performance score with 0.95599 score in Accuracy, Precision, F1-Score, and recall score where as Decision Tree model performs the fastest with 0.09 seconds of runtime and achieving 0.93 score in the performance.
Co-Authors Benfano Soewito