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 …

It is a wide-spread way to detect malware by analyzing its behavioral characteristics based
on API call sequences. However, previous studies usually just focus on its static or dynamic …

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 …

As the amount of malware has grown rapidly in recent years, it has become the most
dominant attack method in network security. Learning execution behavior, especially …

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 cyber ecosystem is facing severe threats from malware attacks, making it imperative to
detect malware to safeguard a purified Internet environment. However, current studies …

Due to the rapid development and widespread presence of malware, deep-learning-based
malware detection methods have become a pivotal approach used by researchers to protect …

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

Dynamic malware analysis that monitors the sequences of API calls of the program in a
sandbox has been proven to be effective against code obfuscation and unknown malware …

Malware is becoming increasingly prevalent in recent years with the widespread
deployment of the information system. Many malicious programs pose a great threat to …