What question did this study set out to answer?

To develop a real-time network attack detection system using LSTM deep learning architecture.

March 24, 2026Open Access

Real-Time Network Attack Detection Using LSTM-Based Deep Learning

Puntos clave

To develop a real-time network attack detection system using LSTM deep learning architecture.
Analyzed network traffic data with LSTM deep learning model.
Evaluated the model using benchmark datasets to ensure effectiveness.
Measured detection accuracy and false positive rates.
Achieved high detection accuracy for identifying cyber-attacks.
Reduced false positive rates compared to traditional methods.
Demonstrated suitability of LSTM for real-time intrusion detection.

Resumen

This study presents a real-time network attack detection system based on Long Short-Term Memory (LSTM) deep learning architecture. The proposed model effectively captures temporal patterns in network traffic data, enabling accurate identification of cyber-attacks. The system was evaluated using benchmark datasets, demonstrating high detection accuracy and reduced false positive rates. The results highlight the suitability of LSTM-based approaches for real-time intrusion detection systems in modern cybersecurity environments.

Leer artículo completoexternamente

Me gusta

Guardar

Ver artículo completo