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 …

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 …

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 …

In recent years, malware has emerged as a critical security threat. In addition, malware
authors continue to embed numerous anti-detection features to evade the existing malware …

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 …

This paper presents API-MalDetect, a new deep learning-based automated framework for
detecting malware attacks in Windows systems. The framework uses an NLP-based encoder …

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 …

The sharing of malicious code libraries and techniques over the Internet has vastly
increased the release of new malware variants in an unprecedented rate. Malware variants …

Conventional malware detection technologies have the limitation to detect malware because
recent malware uses a variety of the avoidance techniques such as obfuscation, packing …

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