Analisis Komparatif Pengaruh Kedalaman Arsitektur Convolutional Neural Network untuk Deteksi Anomali Jaringan

Krisna Nuresa Qodri; Satriawan Desmana; Bella Adinda Putri; Ratih Ratih; Muhammad Abdul Mu'in

doi:10.56211/blendsains.v5i1.1841

Authors

Krisna Nuresa Qodri Politeknik Negeri Cilacap
Satriawan Desmana Politeknik Negeri Cilacap
Bella Adinda Putri Politeknik Negeri Cilacap
Ratih Ratih Politeknik Negeri Cilacap
Muhammad Abdul Mu'in Politeknik Negeri Cilacap

DOI:

https://doi.org/10.56211/blendsains.v5i1.1841

Keywords:

Convolutional Neural Network; Intrusion Detection System; Deteksi Anomali Jaringan; Deep Learning; Keamanan Siber

Abstract

Tingkat kecanggihan serangan siber yang terus meningkat menjadi tantangan serius bagi sistem keamanan jaringan konvensional. Penelitian ini mengkaji efektivitas Convolutional Neural Network (CNN) dalam mendeteksi anomali jaringan menggunakan dataset UNSW-NB15. Dua arsitektur CNN dengan tingkat kompleksitas berbeda dibandingkan, yaitu CNN dasar dengan satu blok konvolusi dan CNN menengah dengan tiga blok konvolusi. Hasil eksperimen menunjukkan bahwa arsitektur CNN menengah memberikan kinerja yang lebih unggul dengan akurasi 97,91%, presisi 98,89%, dan nilai ROC-AUC 0,9979, jauh melampaui model dasar yang hanya mencapai akurasi 94,36%. Tingkat false positive yang rendah, yaitu 1,93%, serta tingkat false negative sebesar 2,18%, mengindikasikan kelayakan praktis yang kuat untuk diterapkan pada lingkungan operasional nyata. Temuan ini memberikan bukti empiris bahwa rekayasa kedalaman arsitektur yang moderat disertai regularisasi kuat memegang peranan krusial dalam menjaga stabilitas konvergensi dan mengeliminasi risiko overfitting ekstrem pada sistem deteksi intrusi jaringan.

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