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 …
A Comprehensive Analysis of Explainable AI for Malware Hunting
In the past decade, the number of malware variants has increased rapidly. Many
researchers have proposed to detect malware using intelligent techniques, such as Machine …
researchers have proposed to detect malware using intelligent techniques, such as Machine …
A survey on explainability of graph neural networks
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have
gained significant attention and demonstrated remarkable performance in various domains …
gained significant attention and demonstrated remarkable performance in various domains …
A wolf in sheep's clothing: practical black-box adversarial attacks for evading learning-based windows malware detection in the wild
Given the remarkable achievements of existing learning-based malware detection in both
academia and industry, this paper presents MalGuise, a practical black-box adversarial …
academia and industry, this paper presents MalGuise, a practical black-box adversarial …
Improving {ML-based} Binary Function Similarity Detection by Assessing and Deprioritizing Control Flow Graph Features
Machine learning-based binary function similarity detection (ML-BFSD) has witnessed
significant progress recently. They often choose control flow graph (CFG) as an important …
significant progress recently. They often choose control flow graph (CFG) as an important …
A survey of explainable graph neural networks for cyber malware analysis
Malicious cybersecurity activities have become increasingly worrisome for individuals and
companies alike. While machine learning methods like Graph Neural Networks (GNNs) …
companies alike. While machine learning methods like Graph Neural Networks (GNNs) …
[HTML][HTML] A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction
Q Yang, X Wang, J Yin, A Du, A Zhang, L Wang… - Geoscience …, 2024 - Elsevier
The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite
vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of …
vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of …
GAGE: Genetic algorithm-based graph explainer for malware analysis
Malware analysts often prefer reverse engineering using Call Graphs, Control Flow Graphs
(CFGs), and Data Flow Graphs (DFGs), which involves the utilization of black-box Deep …
(CFGs), and Data Flow Graphs (DFGs), which involves the utilization of black-box Deep …
Malgne: Enhancing the performance and efficiency of cfg-based malware detector by graph node embedding in low dimension space
The rich semantic information in Control Flow Graphs (CFGs) of executable programs has
made Graph Neural Networks (GNNs) a key focus for malware detection. However, existing …
made Graph Neural Networks (GNNs) a key focus for malware detection. However, existing …
EAGLE: Evasion attacks guided by local explanations against Android malware classification
With machine learning techniques widely used to automate Android malware detection, it is
important to investigate the robustness of these methods against evasion attacks. A recent …
important to investigate the robustness of these methods against evasion attacks. A recent …