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 …

Dynamic malware analysis executes the program in an isolated environment and monitors
its run-time behaviour (eg system API calls) for malware detection. This technique has been …

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 …

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 …

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 analysis methods effectively identify shelled, wrapped, or obfuscated malware,
thereby preventing them from invading computers. As a significant representation of …

Malware stands for malicious software. It is software that is designed with a harmful intent. A
malware detector is a system that attempts to identify malware using Application …

The number of malware is increasing rapidly regardless of the common use of anti-malware
software. Detection of malware continues to be a challenge as attackers device new …

The explosive growth of malware variants poses a continuously and deeply evolving
challenge to information security. Traditional malware detection methods require a lot of …

Nowadays, the rapid growth of the number and variety of malware brings great security
challenges. Machine learning has become a mainstream tool for effective malware …