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 …

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

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 …

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 …

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

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 …

The widespread development of the malware industry is considered the main threat to our e-
society. Therefore, malware analysis should also be enriched with smart heuristic tools that …

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 …

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 …