Neural Network Implementation for Anomaly Detection of Brute Force Attacks in Intrusion Detection Systems
Implementasi Neural Network untuk Anomaly Detection pada Serangan Brute Force pada Sistem Deteksi Intrusi
Abstract
Pada penelitian ini telah dilakukan uji efektivitas neural network dalam Sistem Deteksi Intrusi (IDS) pada data serangan brute force. Melalui uji coba tersebut, penelitian mengidentifikasi parameter-parameter optimal yang dapat menghasilkan hasil yang akurat dalam deteksi intrusi. Hasil penelitian mengungkapkan beberapa parameter neural network akan mencapai hasil yang optimal jika parameter learning rate sebesar 0,1, proporsi data latih dan data uji sebesar 80:20, dan jumlah optimal node dalam hidden layer 4. Parameter lain seperti minimum error sebesar 0,0001 dan iterasi sebanyak 2500 juga memainkan peran krusial dalam meningkatkan kemampuan IDS. Berdasarkan penelitian menunjukan model neural network dapat memberikan hasil optimal dalam mendeteksi pola intrusi. Penelitian ini dapat membantu dalam pengembangan IDS berbasis neural network yang handal dan efisien untuk mengatasi tantangan deteksi intrusi.
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Copyright (c) 2025 Ahmad Heryanto, Adi Hermansyah, Triwanda Septian, Ali Bardadi
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