Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …

Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …

With the rapid increase in the number of malware, the detection and classification of
malware have become more challenging. In recent years, many malware classification …

The robustness of deep neural networks is crucial to modern AI-enabled systems and
should be formally verified. Sigmoid-like neural networks have been adopted in a wide …

The proliferation of malware has exhibited a substantial surge in both quantity and diversity,
posing significant threats to the Internet and indispensable network applications. The …

Deep learning (DL)-based malware detectors have shown promise in swiftly detecting
unseen malware without expensive dynamic malware behavior analysis. These detectors …

The robustness of neural networks is fundamental to the hosting system's reliability and
security. Formal verification has been proven to be effective in providing provable …

Inspired by the success of graph neural network in graph data classification, graph neural
networks have been widely used in malware classification and they have been proven to be …

Detecting out-of-distribution (OOD) data categories while preserving the accuracy of existing
classifications is a pressing challenge in many domains. Conventional methods often falter …