Malware Representation

Malware representation research focuses on developing effective ways to encode malware characteristics for accurate and efficient classification and detection. Current efforts concentrate on improving model performance with limited labeled data using techniques like few-shot learning and semi-supervised learning, often incorporating multiple data modalities (e.g., API calls, network traffic, images derived from malware behavior) and leveraging advanced architectures such as deep learning models and large language models. These advancements are crucial for combating the ever-evolving threat landscape of malware, enabling faster and more robust detection of both known and unknown malicious software.