Pendekatan Metode Ensemble Learning untuk Deteksi Serangan DDoS menggunakan Soft Voting Classifier

Steven Joses, Stefanie Quinevera, Ricky Mardianto, Donata Yulvida, Ary Mazharuddin Shiddiqi

Abstract


Serangan Distributed Denial of Service (DDoS) adalah jenis serangan yang kompleks dan sering melibatkan berbagai pola lalu lintas jaringan yang berbeda. Model soft voting classifier dapat menggabungkan hasil dari beberapa model klasifikasi yang berbeda, sehingga meningkatkan kemampuan untuk mendeteksi dan mengatasi serangan DDoS dengan berbagai pola dan skenario yang berbeda. Dengan memanfaatkan model soft voting classifier berdasarkan fitur-fitur yang mendukung, dapat meningkatkan ketahanan sistem terhadap serangan DDoS dengan lebih efektif, mengurangi dampaknya, dan memastikan ketersediaan sumber daya jaringan dan layanan internet bagi pengguna yang mengaksesnya. Data yang digunakan dalam penelitian ini menggunakan dataset DDoS yang diperoleh dari situs kaggle.com. Dataset ini memiliki 23 atribut termasuk satu variabel output dengan jumlah data sebanyak 104.245 record. Dilakukan preprocessing pada dataset kemudian diklasifikasi menggunakan lima model machine learning dan sepuluh ensemble learning method untuk mendapatkan hasil akurasi tertinggi. Hasil pengujian menunjukkan bahwa ensemble method sangat optimal dalam mendeteksi serangan DDoS baik menggunakan fitur berdasarkan Information Gain maupun menggunakan fitur berdasarkan Gain Ratio dibandingkan dengan metode machine learning tunggal.


Keywords


DDoS; Soft Voting Classifier; Deteksi Serangan DDoS; Machine Learning; Information Gain; Gain Ratio; Feature Selection

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References


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DOI: http://dx.doi.org/10.26418/jp.v10i1.73241

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