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

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.