Computer Networks Cybersecurity Monitoring Based on Deep Learning Model

Abstract

Effective cybersecurity monitoring is essential for safeguarding computer networks against evolving threats. However, the increasing scale, complexity, and data volume of modern networks pose significant challenges to traditional monitoring methods. To address these challenges, this article proposes a deep learning‐based approach for computer network cybersecurity monitoring. Leveraging the MnasNet‐LSTM model, network traffic data is classified into distinct categories, including normal traffic and cyberattacks. The model is trained using the CICIDS2017 dataset, yielding promising results with a classification accuracy of approximately 97.05% and a minimal error rate of 8.46%.