The rich semantic information in Control Flow Graphs (CFGs) of executable programs has
made Graph Neural Networks (GNNs) a key focus for malware detection. However, existing …

Traditional malware detection methods cannot keep up with the massive amount of newly
created malware quickly and effectively. Machine learning is a promising method for the …

Malware have been one of the biggest cyber threats in the digital world for a long time.
Existing machine learning based malware classification methods rely on handcrafted …

The surge of malware poses a huge threat to cyberspace security. The existing malware
analysis methods based on machine learning mainly rely on feature engineering. These …

Malware is a significant threat to the security of computer systems and networks, requiring
sophisticated techniques to analyze its behavior and functionality for detection. Due to their …

Managing the threat posed by malware requires accurate detection and classification
techniques. Traditional detection strategies, such as signature scanning, rely on manual …

Malware detection is a vital task for cybersecurity. For malware dynamic behavior, threats
come from a small number of Application Programming Interfaces (APIs) embedded in the …

Malicious software, widely known as malware, is one of the biggest threats to our
interconnected society. Cybercriminals can utilize malware to carry out their nefarious tasks …

With society's increasing reliance on computer systems and network technology, the threat
of malicious software grows more and more serious. In the field of information security …

With the ever increasing threat of malware, extensive research effort has been put on
applying Deep Learning for malware classification tasks. Graph Neural Networks (GNNs) …