DETEKSI SERANGAN SIBER PADA PERANGKAT KESEHATAN BERBASIS WIFI DAN MQTT DENGAN MACHINE LEARNING

Roymond Chandra Pradana, Alva Hendi Muhammad

Abstract


Perangkat kesehatan yang tergabung dalam Internet of Medical Things (IoMT) rentan terhadap serangan siber, terutama saat menggunakan protokol komunikasi seperti WiFi dan MQTT. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis serangan pada perangkat IoMT serta mengembangkan model deteksi yang efektif berbasis machine learning. Metode yang digunakan meliputi pengumpulan data dari dataset terbuka, preprocessing data, dan penerapan berbagai algoritma machine learning seperti Random Forest, SVM, KNN, LightGBM, SGD Classifier, CatBoost, dan XGBoost. Hasil pengujian menunjukkan model yang dikembangkan memiliki tingkat akurasi tinggi, yakni 99,5% untuk deteksi dua kategori serangan, 91,5% untuk enam kategori, dan 86,9% untuk sembilan belas kategori. Temuan ini membuktikan bahwa machine learning dapat meningkatkan deteksi serangan siber pada perangkat medis secara signifikan. Penelitian ini memberikan kontribusi penting bagi keamanan IoMT dengan menerapkan teknik machine learning yang canggih. Selain itu, studi ini menekankan pentingnya inovasi dalam mendeteksi serangan siber serta memberikan rekomendasi untuk pengembangan algoritma yang lebih efisien di masa depan.

Keywords


serangan siber; machine learning; Internet of Medical Things; deteksi serangan

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References


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DOI: https://doi.org/10.29100/jipi.v10i4.7489

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