Deep learning for zero-day malware detection and classification: A survey
Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …
malware software can catch it. This novelty and the lack of existing mitigation strategies …
A survey on malware detection with graph representation learning
Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …
complexity of malware. Traditional detection methods based on signatures and heuristics …
GuardHealth: Blockchain empowered secure data management and Graph Convolutional Network enabled anomaly detection in smart healthcare
The paradox between the dramatic development of medical data privacy demand and years
of bureaucratic regulation has slowed innovation for electronic medical records (EMRs). We …
of bureaucratic regulation has slowed innovation for electronic medical records (EMRs). We …
[HTML][HTML] A systematic literature review on windows malware detection: Techniques, research issues, and future directions
The aim of this systematic literature review (SLR) is to provide a comprehensive overview of
the current state of Windows malware detection techniques, research issues, and future …
the current state of Windows malware detection techniques, research issues, and future …
An ensemble of pre-trained transformer models for imbalanced multiclass malware classification
Classification of malware families is crucial for a comprehensive understanding of how they
can infect devices, computers, or systems. Hence, malware identification enables security …
can infect devices, computers, or systems. Hence, malware identification enables security …
A new approach for APT malware detection based on deep graph network for endpoint systems
C Do Xuan, DT Huong - Applied Intelligence, 2022 - Springer
The form of spreading malware through end-users and thereby escalating and stealing data
in organizations is one of the attack techniques widely used by Advanced Persistent Threat …
in organizations is one of the attack techniques widely used by Advanced Persistent Threat …
Malware detection based on graph attention networks for intelligent transportation systems
Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable,
and environmentally friendly without detrimentally affecting the service quality. ITS can face …
and environmentally friendly without detrimentally affecting the service quality. ITS can face …
FewM-HGCL: Few-shot malware variants detection via heterogeneous graph contrastive learning
Malware variant attacks have been becoming serious threats in the Internet ecosystem.
However, prior arts on malware variants detection over-rely on the supervised learning …
However, prior arts on malware variants detection over-rely on the supervised learning …
Features engineering to differentiate between malware and legitimate software
Malware is the primary attack vector against the modern enterprise. Therefore, it is crucial for
businesses to exclude malware from their computer systems. The most responsive solution …
businesses to exclude malware from their computer systems. The most responsive solution …
Comparing deep learning and shallow learning techniques for api calls malware prediction: A study
Recognition of malware is critical in cybersecurity as it allows for avoiding execution and the
downloading of malware. One of the possible approaches is to analyze the executable's …
downloading of malware. One of the possible approaches is to analyze the executable's …