Deep learning for zero-day malware detection and classification: A survey

F Deldar, M Abadi - ACM Computing Surveys, 2023 - dl.acm.org
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 …

A survey on malware detection with graph representation learning

T Bilot, N El Madhoun, K Al Agha, A Zouaoui - ACM Computing Surveys, 2024 - dl.acm.org
Malware detection has become a major concern due to the increasing number and
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

Z Wang, N Luo, P Zhou - Journal of Parallel and Distributed Computing, 2020 - Elsevier
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 …

[HTML][HTML] A systematic literature review on windows malware detection: Techniques, research issues, and future directions

P Maniriho, AN Mahmood, MJM Chowdhury - Journal of Systems and …, 2024 - Elsevier
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 …

An ensemble of pre-trained transformer models for imbalanced multiclass malware classification

F Demirkıran, A Çayır, U Ünal, H Dağ - Computers & Security, 2022 - Elsevier
Classification of malware families is crucial for a comprehensive understanding of how they
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 …

Malware detection based on graph attention networks for intelligent transportation systems

C Catal, H Gunduz, A Ozcan - Electronics, 2021 - mdpi.com
Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable,
and environmentally friendly without detrimentally affecting the service quality. ITS can face …

FewM-HGCL: Few-shot malware variants detection via heterogeneous graph contrastive learning

C Liu, B Li, J Zhao, Z Zhen, X Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

Features engineering to differentiate between malware and legitimate software

AY Daeef, A Al-Naji, AK Nahar, J Chahl - Applied Sciences, 2023 - mdpi.com
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 …

Comparing deep learning and shallow learning techniques for api calls malware prediction: A study

A Cannarile, V Dentamaro, S Galantucci, A Iannacone… - Applied Sciences, 2022 - mdpi.com
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 …