Deteksi Serangan Pada Jaringan Internet Of Things Medis Menggunakan Machine Learning Dengan Algoritma XGBoost
Attack Detection On Internet Medical Of Things Using Machine Learning With Xgboost Algorithm
DOI:
https://doi.org/10.14421/csecurity.2025.8.1.5036Abstract
Internet of Things (IoT) telah memberikan dampak besar pada sektor kesehatan, memungkinkan pengumpulan data pasien secara real-time dan meningkatkan efisiensi layanan kesehatan. Namun, adopsi perangkat IoT medis juga membawa tantangan baru terkait keamanan, terutama serangan Distributed Denial of Service (DDoS) yang dapat mengganggu layanan kritis. Penelitian ini melakukan deteksi terhadap lima jenis serangan, yaitu ARP Spoofing, Recon Attack, MQTT Attack, TCP/IP DoS, dan DDoS, menggunakan model machine learning dengan algoritma XGBoost. Dataset yang digunakan adalah CICIoMT2024, yang dirancang khusus untuk menilai keamanan perangkat medis terhubung, melibatkan 40 perangkat IoMT. XGBoost menunjukkan performa terbaik dengan akurasi, recall, presisi, dan F1-score yang unggul, mencapai akurasi 99.8%, presisi 92.4%, recall 96%, dan F1-score 93.8%. Sebelumnya, algoritma lain seperti Logistic Regression dan Naive Bayes menunjukkan akurasi masing-masing sebesar 79% dan 92% dalam mendeteksi serangan serupa, hal ini menunjukan keterbatasan dalam menangani pola yang lebih kompleks. Hasil ini menegaskan efektivitas XGBoost dalam mendeteksi ancaman keamanan dalam ekosistem IoT medis, memberikan perlindungan lebih baik terhadap potensi gangguan pada layanan kesehatan kritis.
Kata kunci: Machine Learning, Keamanan Siber, xgboost, deteksi, Internet Medical of Things
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Abstract
The Internet of Things (IoT) has significantly impacted the healthcare sector, enabling real-time patient data collection and enhancing service efficiency. However, the adoption of medical IoT devices also introduces new security challenges, particularly Distributed Denial of Service (DDoS) attacks that can disrupt critical services. This study detects five types of attacks: ARP Spoofing, Recon Attack, MQTT Attack, TCP/IP DoS, and DDoS, using machine learning models with the XGBoost algorithm. The dataset used is CICIoMT2024, specifically designed to assess the security of connected medical devices, involving 40 IoMT devices. XGBoost demonstrated the best performance with superior accuracy, recall, precision, and F1-score, achieving 99.8% accuracy, 92.4% precision, 96% recall, and 93.8% F1-score. Previously, other algorithms such as Logistic Regression and Naive Bayes showed accuracies of 79% and 92% respectively in detecting similar attacks, but with limitations in handling more complex patterns. These results underscore the effectiveness of XGBoost in detecting security threats in the medical IoT ecosystem, providing enhanced protection against potential disruptions to critical healthcare services.
Keywords: Machine Learning, Cybersecurity, xgboost, detection, Internet Medical of Things
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