Multimodal Machine Learning Improves Malware Classification Techniques

🦠 Multimodal Machine Learning Enhances Malware Classification Techniques. This research explores the effectiveness of multimodal machine learning approaches for classifying malware, focusing on the structured Windows Portable Executable (PE) file format. The study employs Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models, training them on features from various sections of PE files, including headers and entire files. Results indicate that multimodal models, which utilize distinct parts of PE files, outperform traditional baseline models. This advancement highlights the potential for improved malware detection methods in computer networks and systems.

Source

Original