Multi-modal malware classification with hierarchical consistency and saliency-constrained adversarial training

This paper discusses the growing challenge of malware (malicious software designed to exploit computer system vulnerabilities) detection, noting that over 450,000 new malware samples are detected daily as of 2024. Traditional detection methods like signature-based detection (matching known byte patterns against a database) and behavior-based detection (running malware in isolated test environments to observe its actions) have limitations: signature-based methods fail against new or disguised malware, while behavior-based methods are computationally expensive and can be evaded by malware that detects virtual environments. The paper proposes using machine learning and deep learning approaches trained on features from both static and dynamic analysis to better classify files as malicious or benign.