Multimodal Deep Learning for Malware Behavior Detection with Integrated Database Storage Optimization (Informatica, 2025)
With the rapid development of Internet technology, the types of malwares are constantly increasing, which brings significant challenges to network security. Traditional malware detection methods mainly rely on feature extraction and classifier design, but these methods have certain limitations when dealing with complex and changeable malware behaviors. To solve this problem, this study proposes a malware behavior detection method based on multi-modal deep learning combined with database storage optimization technology. This method will extract multi-dimensional malware features and utilize deep learning models for learning and classification to improve the accuracy and efficiency of detection. The experimental data results show that the proposed method in this study is highly accurate and robust in malware behavior detection. By detecting 1000 malware samples, the method in this study can accurately identify the behavioral characteristics of 950 of them and effectively classify them, with a detection accuracy of up to 95%. Compared with other traditional malware detection methods, the detection accuracy of traditional methods is 70% on average, while the method in this study can reach more than 90%. In terms of false reporting rate, the traditional method is about 30%, but the method in this study can be controlled within 5%. In terms of false alarm rate, the traditional method is about 20%, but the method in this study can be reduced to about 3%, which shows apparent advantages. By introducing database storage optimization technology, the method in this study can not only improve the accuracy of malware detection but also effectively reduce the storage pressure of the database, reduce the storage space of the database by about 40% compared with that before optimization, improve the running efficiency of the system, and shorten the overall response time of the system by about 30%.