Inovasi Model Intrusion Detection System (IDS) menggunakan Double Layer Gated Recurrent Unit (GRU) dengan Fitur Berbasis Fusion
Abstract
Intrusion Detection System (IDS) merupakan komponen penting dalam menjaga keamanan jaringan dari ancaman siber. Dengan meningkatnya jumlah dan kompleksitas serangan, diperlukan metode deteksi yang lebih akurat dan efisien. Dalam penelitian ini, diusulkan model IDS berbasis Double Layer Gated Recurrent Unit (GRU) yang dirancang untuk meningkatkan akurasi deteksi dan mengurangi kesalahan prediksi. Arsitektur GRU ganda memungkinkan pengambilan fitur temporal yang lebih baik dari data lalu lintas jaringan. Model ini diuji menggunakan dataset standar IDS, dan hasil eksperimen menunjukkan bahwa metode ini mampu mencapai tingkat akurasi yang lebih tinggi dibandingkan dengan model GRU tunggal dan metode pembelajaran mesin konvensional. Selain itu, penerapan proses feature fusion di antara dua lapisan GRU memberikan kontribusi signifikan terhadap peningkatan akurasi dan pengurangan tingkat false positive rate (FPR). Temuan ini mengindikasikan bahwa arsitektur yang diusulkan efektif dalam mendeteksi serangan jaringan secara real-time dengan efisiensi komputasi yang lebih baik.
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DOI: https://doi.org/10.21107/edutic.v12i1.28822
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