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 …

In this paper, a new deep learning and machine learning combined model is proposed for
malware behavior analysis. One part of it analyzes the dependency relation in API …

Cybercriminals are constantly developing new techniques to circumvent the security
measures implemented by experts and researchers, so malware is able to evolve very …

Modern malware detection tools rely on special permissions to collect data that can reveal
the presence of suspicious software within a machine. Typical data that they collect for this …

Malware API call graph derived from API call sequences is considered as a representative
technique to understand the malware behavioral characteristics. However, it is troublesome …

This research study mainly focused on the dynamic malware detection. Malware
progressively changes, leading to the use of dynamic malware detection techniques in this …

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 …

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 …

The number of malware, including variants and new types, is dramatically increasing over
the years, posing one of the greatest cybersecurity threats nowadays. To counteract such …

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