Pemilihan Fitur Menggunakan Chi-Square Untuk Deteksi Serangan Pada Jaringan Internet of Medical Things Menggunakan Random Forest
DOI:
https://doi.org/10.47065/jimat.v5i2.478Keywords:
IoMT; CICIoMT2024; Chi-SquareAbstract
This research discusses the security of the Internet of Medical Things (IoMT) network using the CICIoMT2024 dataset to analyze and detect cyber attacks through the application of Machine Learning (ML) methods. This research uses the Chi-Square feature selection technique to identify important features, and uses the Random Forest algorithm for the data classification process. The utilization of Chi-Square features, especially in the analysis of network traffic of IoT devices, has not been widely explored, so this research makes a new contribution to the field. The result of the Chi-Square feature selection are used to train Machine Learning models to classify data between normal traffic and traffic containing attacks. In the experiments conducted, the Random Forest algorithm showed excellent performance by achieving up to 100% accuracy, as well as high precision, recall, F1-Score values. These results show that Random Forest is able to handle the complexity of IoMT data effectively. Thus, it can be concluded that the Random Forest algorithm is very relevant and effective to use in IoMT network security research.
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