Klasifikasi Serangan Denial of Service pada Jaringan Internet of Things Menggunakan Random Forest: Studi pada Dataset Gotham 2025
DOI:
https://doi.org/10.59563/djtech.v7i1.433Keywords:
Denial of Service, Gotham 2025, Internet of Things, Klasifikasi Serangan, Random ForestAbstract
Serangan Denial of Service (DoS) pada jaringan Internet of Things (IoT) dapat mengganggu ketersediaan layanan dan komunikasi antarperangkat, sehingga diperlukan metode klasifikasi yang mampu membedakan lalu lintas normal dan lalu lintas serangan secara akurat. Penelitian ini bertujuan mengimplementasikan algoritma Random Forest untuk mengklasifikasikan serangan DoS pada jaringan IoT menggunakan dataset Gotham 2025. Penelitian dilakukan melalui eksperimen komputasional dengan dataset yang terdiri atas 2.500.000 sampel dan lima kelas, yaitu dos-http, dos-icmp, dos-syn, dos-udp, dan Normal. Data dibagi menggunakan rasio 80:20, dengan 2.000.000 sampel untuk pelatihan dan 500.000 sampel untuk pengujian. Evaluasi model dilakukan menggunakan confusion matrix, classification report, serta kurva ROC dan nilai AUC, dengan penerapan 5-fold cross-validation. Hasil pengujian menunjukkan bahwa model memperoleh akurasi sebesar 99%, dengan nilai macro F1-score dan weighted F1-score masing-masing sebesar 0,99. Kelas dos-http, dos-icmp, dan dos-syn memperoleh precision, recall, dan F1-score sebesar 1,00, sedangkan kelas dos-udp memiliki recall 0,99 dan kelas Normal memiliki precision 0,99. Nilai AUC untuk seluruh kelas mencapai 1,00. Hasil tersebut menunjukkan bahwa Random Forest mampu mengklasifikasikan lima kelas pada dataset dengan kinerja tinggi. Namun, pengujian pada lalu lintas jaringan nyata dan dataset eksternal masih diperlukan untuk menilai kemampuan generalisasi serta kelayakan penerapannya dalam lingkungan operasional.
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