.png){kind=icon}
返回
A Survey on Malware Detection with Graph Representation Learning
.png){kind=icon}
OA
AI
DOI：10.1145/3664649.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. Recently, the application of Graph Representation Learning (GRL) techniques on graph-structured data has demonstrated impressive capabilities in malware detection. This success benefits notably from the robust structure of graphs, which are challenging for attackers to alter, and their intrinsic explainability capabilities. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures such as Function Call Graphs (FCGs) and Control Flow Graphs (CFGs). This study also discusses the robustness of GRL-based methods to adversarial attacks, contrasts their effectiveness with other ML/DL approaches, and outlines future research for practical deployment.
Keyword:
Deep learning
DL
GNN
graph neural networks
graph representation learning
machine learning
malware
malware detection
ML
期刊
IF:
28
论文数:
2.5K
被引数:
3.5W
机构
引用论文
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
OAAI分享收藏Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs基于图变分自动编码器的恶意软件检测框架从API调用图中提取嵌入OAAI
OAAI分享收藏Ligand-Free Palladium-Catalyzed Oxyarylation of Dihydronaphthalenes and Chromenequinone with o-Iodophenols and 3-Iodolawsone in PEG-400: An Efficient Synthesis of 5-Carbapterocarpans and Pterocarpanquinones.png)
SynthesisOAAI
SynthesisIF0
OAAI分享收藏
查看更多内容