Model Jaringan Neural Untuk Deteksi Anomali Pada Sistem Keamanan (SIBER): Rancangan, Implementasi, dan Analisis (Universitas Teknologi Muhammadiyah Jakarta (UTM Jakarta), 2025)
The increase in complex and dynamic cyber attacks drives the need for artificial intelligence-based anomaly detection systems. This article develops a neural network model to detect anomalies in cyber security systems utilizing a Research and Development approach. The model is developed using deep learning approaches (Autoencoder and LSTM) and evaluated against real-world network traffic data. The results demonstrate high effectiveness in detecting intrusions in real-time. This model introduces a technology-based innovation with a positive impact on the national digital security landscape.