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 …

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 …

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 …

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 …

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

Application Programming Interfaces (APIs) are still considered the standard accessible data
source and core wok of the most widely adopted malware detection and classification …

As computing systems become increasingly advanced and as users increasingly engage
themselves in technology, security has never been a greater concern. In malware detection …

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 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 …

Basically malware detection techniques are either: static analysis or dynamic analysis. Static
analysis explores malware code without executing it while dynamic analysis relies on run …