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

Year: 2026

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19160360

Author: Ahmed Nabil Mohamed Fouad

Institution: Karary University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7140028778

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