Application of Deep Learning Models for Real-Time Automatic Malware Detection

The increase in the sophistication and volume of cyberattacks has made traditional malware detection methods, such as those based on signatures and heuristics, obsolete. These conventional techniques struggle to identify new malware variants that employ advanced evasion tactics, resulting in significant security gaps. This study addresses this problem by proposing a hybrid model based on deep learning that integrates static and dynamic analysis to improve the precision and robustness of malware detection. This proposal combines the extraction of static features from the code and dynamic features from the behavior at runtime, using convolutional neural networks for visual analysis and recurrent neural networks for sequential analysis. This comprehensive integration of features allows our model to detect known malware and new variants more effectively. The results show that our model achieves a precision of 98%, a recall of 97%, and an F1-score of 0.975, outperforming traditional methods, which generally reach 88% to 89% precision. Furthermore, our model outperforms recent deep learning approaches documented in the literature, which report up to 96% precision. In work, it offers a significant advancement in malware detection, providing a more effective and adaptable solution to modern cyber threats.