A key review on graph data science: The power of graphs in scientific studies

R Das, M Soylu - Chemometrics and Intelligent Laboratory Systems, 2023 - Elsevier
This comprehensive review provides an in-depth analysis of graph theory, various graph
types, and the role of graph visualization in scientific studies. Graphs serve as powerful tools …

DawnGNN: Documentation augmented windows malware detection using graph neural network

P Feng, L Gai, L Yang, Q Wang, T Li, N Xi, J Ma - Computers & Security, 2024 - Elsevier
Abstract Application Program Interface (API) calls are widely used in dynamic Windows
malware analysis to characterize the run-time behavior of malware. Researchers have …

[HTML][HTML] A new framework for visual classification of multi-channel malware based on transfer learning

Z Zhao, S Yang, D Zhao - Applied Sciences, 2023 - mdpi.com
With the continuous development and popularization of the Internet, there has been an
increasing number of network security problems appearing. Among them, the rapid growth …

API2Vec: Learning Representations of API Sequences for Malware Detection

L Cui, J Cui, Y Ji, Z Hao, L Li, Z Ding - Proceedings of the 32nd ACM …, 2023 - dl.acm.org
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 …

MCTVD: A malware classification method based on three-channel visualization and deep learning

H Deng, C Guo, G Shen, Y Cui, Y Ping - Computers & Security, 2023 - Elsevier
With the rapid increase in the number of malware, the detection and classification of
malware have become more challenging. In recent years, many malware classification …

Global-Local Attention-Based Butterfly Vision Transformer for Visualization-Based Malware Classification

MM Belal, DM Sundaram - IEEE Access, 2023 - ieeexplore.ieee.org
In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic
techniques for visualization-based malware classification and detection. Though vision …

CTIMD: Cyber threat intelligence enhanced malware detection using API call sequences with parameters

T Chen, H Zeng, M Lv, T Zhu - Computers & Security, 2024 - Elsevier
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 …

Guided Malware Sample Analysis based on Graph Neural Networks

YH Chen, SC Lin, SC Huang, CL Lei… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Malicious binaries have caused data and monetary loss to people, and these binaries keep
evolving rapidly nowadays. With tons of new unknown attack binaries, one essential daily …

A Survey on Malware Detection with Graph Representation Learning

T Bilot, NE Madhoun, KA Agha, A Zouaoui - arXiv preprint arXiv …, 2023 - arxiv.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 …

[HTML][HTML] Dynamic Malware Analysis Based on API Sequence Semantic Fusion

S Zhang, J Wu, M Zhang, W Yang - Applied Sciences, 2023 - mdpi.com
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 …