Analyzing malware based on API call sequences is an effective approach, as these
sequences reflect the dynamic execution behavior of malware. Recent advancements in …

Analyzing malware based on API call sequence is an effective approach as the sequence
reflects the dynamic execution behavior of malware. Recent advancements in deep learning …

Abstract Application Programming Interfaces (APIs) are widely considered a useful data
source for dynamic malware analysis to understand the behavioral characteristics of …

Learning on execution behaviour, ie, sequences of API calls, is proven to be effective in
malware detection. In this paper, we present CruParamer, a deep neural network based …

The growing variety of malicious software, ie, malware, has caused great damage and
economic loss to computer systems. The API call sequence of malware reflects its dynamic …

In recent years, studies on malware analysis have noticeably increased in the cybersecurity
community. Most recent studies concentrate on malware classification and detection or …

The existing dynamic malware detection methods based on API call sequences ignore the
semantic information of functions. Simply mapping API to numerical values does not reflect …

Dynamic malware detection executes the software in a secured virtual environment and
monitors its run-time behavior. This technique widely uses API sequence analysis to identify …

Malware in today's business world has become a powerful tool used by cyber attackers. It
has become more advanced, spreading quickly and causing significant harm. Modern …

Malware classification stands as a crucial element in establishing robust computer security
protocols, encompassing the segmentation of malware into discrete groupings. Recently, the …