Injection Attack Detection on Internet of Things Device with Machine Learning Method - Pohan, Mara Muda; Soewito, Benfano (STIKOM Tunas Bangsa Pematangsiantar, 2023)
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.