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All Journal Informatik : Jurnal Ilmu Komputer Jurnal Ilmiah Edutic : Pendidikan dan Informatika
Wijaya, Mochamad Rozikul
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Computer Science & IT Education

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Inovasi Model Intrusion Detection System (IDS) menggunakan Double Layer Gated Recurrent Unit (GRU) dengan Fitur Berbasis Fusion Wijaya, Mochamad Rozikul
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.28822

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.
Towards Interpretable Intrusion Detection: A Double-Layer GRU with Feature Fusion Explained by SHAP and LIME Wijaya, Mochamad Rozikul; M. Hanafi
Informatik : Jurnal Ilmu Komputer Vol 21 No 3 (2025): Desember 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i3.12187

Abstract

Computer network security has become increasingly important with the growing complexity of cyberattacks. Deep learning-based Intrusion Detection Systems (IDS) represent a potential solution due to their capability to capture sequential patterns in network traffic. This study proposes a Double-Layer GRU-based IDS with Feature Fusion to enhance the representation of both numerical and categorical data in the NSL-KDD dataset. The training process employs systematic preprocessing techniques, including normalization and one-hot encoding. Experimental results demonstrate high accuracy and generalization with stable performance on both training and testing data, as well as competitive macro F1-scores for multi-class attack detection. Furthermore, interpretability aspects are explored through Explainable Artificial Intelligence (XAI) methods using SHAP and LIME. SHAP provides global insights into the contributions of important features, while LIME explains the influence of features at the local level for individual predictions. The integration of both methods not only enhances transparency and trust in the IDS but also offers deeper insights into dominant attributes in detecting attack patterns. Accordingly, this study contributes to the development of IDS that are accurate, interpretable, and applicable to modern network security.
Co-Authors Hanafi Hanafi