Enhancing Web Security For Threat Detection Through Convolutional Neural Networks

This article introduces a malware detection system based on deep learning, which focuses on malware classification in executable files. Instead of traditional methods, this system emphasizes static analysis between dangerous and harmless files. This requires a 1D-CNN, which is apt for analyzing portable executable files. Static analysis is critical because it detects features in the executable header, thus being a safer method compared to dynamic detection methods, which require specific environments, like virtual machines. The system is integrated into an online platform with a user-friendly web interface, thus easily accessible for users. Extensive comparisons with state-of-the-art models on three very different datasets show the excellence of this approach and establish a reliable, efficient, and practical solution for file executable identification. The presented system ensures speed and precision, providing an interesting point of advancement in malware detection toward easy deployment and use in real time for protection against malicious threats.