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All Journal JiTEKH (Jurnal Ilmiah Teknologi Harapan)
Gultom, Fransko
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Implementasi Recurrent Neural Network Sebagai IDS Terhadap Serangan Jaringan Gultom, Fransko; Siregar, Rosyidah
JiTEKH Vol 12 No 2 (2024): September 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v12i2.996

Abstract

In recent years, a new term has emerged which is now widely applied as IDS (Intrusion Detection System), namely Deep Learing. One type of Deep – Learing is RNN (Recurrent Neural Network) which has recently been applied to IDS. Cyber attacs cannot be avoided, but they can be anticipated by building a system than can detect the performance of network data flows so that users can avoid all kinds of attacks and intrusion attempts from unknown parties. This research aims to test and analyse the accurary and speed of the Recurrent Neural Network in detecting attacks. The method used for this research is RNN, which is operated through the Python and Google Colab programs. Based on the results, the model was trained with 50 epochs resulting in an accurary of 92%. Meanwhile, a model with 30 epochs produces an accurary of 99%. So, the model can work well on training data with a total of 30 epochs.
Co-Authors Siregar, Rosyidah