Application of deep learning in malware detection: a review

Abstract

Abstract The defense of malware remains an important research hotspot in the field of cyberspace security. Recognizing its profound research significance, our defense against malware is still an important research hotspot in the field of cyberspace security. According to several recent surveys, global infrastructure is increasingly attacked by cyber crimes, and the damage of various malicious attacks to countries and even individuals cannot be underestimated, even on the rise. There is an urgent need to adopt advanced tools for early detection of malware and its variants to help researchers take early steps to defend against it. Its broad approach will help the early malware to detect and identify the behavioral patterns of large amounts of malicious data, and the discipline of artificial intelligence offers broad research potential. The results of these tests will help researchers make decisions and early detection, effectively defense against malware. This work compares and reports a classification of malware detection work based on deep learning algorithms. The 2011–2025 articles were considered, and the latest work focused on the literature for the 2018–2025 years; after screening, 72 articles were selected for the initial study. Future researchers will benefit from this review by better understanding current deep learning models in the field of malware detection. The review includes common methods such as convolutional neural networks, recurrent neural networks and generative adversarial networks, focusing on feature extraction techniques such as sequence features, image visualization and data enhancement. The survey summarizes the metrics used to report the accuracy. In addition, it highlights prominent publishers, journals and conferences as platforms for the evaluation of academic works. Taken together, this will help researchers at the current stage gain insight into the unresolved challenges or barriers faced by previous researchers. Among these, the most common problem is the lack of broader and consistent datasets, followed by the need for existing models for further improvement.

Keywords

• Malware
• Malware classification
• Malware detection
• Deep learning
• Review