Exploiting graph-structured data has many real applications in domains including natural language semantics, programming language processing, and malware analysis. A variety of methods has been developed to deal with such data. However, learning graphs of large-scale, varying shapes and sizes is a big challenge for any method. In this paper, we propose a multi-view multi-layer convolutional neural network on labeled directed graphs (DGCNN), in which convolutional filters are designed flexibly to adapt to dynamic structures of local regions inside graphs. The advantages of DGCNN are that we do not need to align vertices between graphs, and that DGCNN can process large-scale dynamic graphs with hundred thousands of nodes. To verify the effectiveness of DGCNN, we conducted experiments on two tasks: malware analysis and software defect prediction. The results show that DGCNN outperforms the baselines, including several deep neural networks.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neunet.2018.09.001 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon.
NPJ Biofilms Microbiomes
October 2024
Net Zero and Resilient Farming, Rothamsted Research, North Wyke, EX20 2SB, UK.
Metagenomics can provide insight into the microbial taxa present in a sample and, through gene identification, the functional potential of the community. However, taxonomic and functional information are typically considered separately in downstream analyses. We develop interpretable machine learning (ML) approaches for modelling metagenomic data, combining the biological representation of species with their associated genetically encoded functions within models.
View Article and Find Full Text PDFA novel approach for APT attack detection based on an advanced computing.
Sci Rep
September 2024
National Institute of Digital Technology and Digital Transformation, Ministry of Information and Communications, Hanoi, Vietnam.
To enhance the effectiveness of the Advanced Persistent Threat (APT) detection process, this research proposes a new approach to build and analyze the behavior profiles of APT attacks in network traffic. To achieve this goal, this study carries out two main objectives, including (i) building the behavior profile of APT IP in network traffic using a new intelligent computation method; (ii) analyzing and evaluating the behavior profile of APT IP based on a deep graph network. Specifically, to build the behavior profile of APT IP, this article describes using a combination of two different data mining methods: Bidirectional Long Short-Term Memory (Bi) and Attention (A).
View Article and Find Full Text PDFProcGCN: detecting malicious process in memory based on DGCNN.
PeerJ Comput Sci
August 2024
College of Cryptographic Engineering, Information Engineering University, Zhengzhou, Henan, China.
The combination of memory forensics and deep learning for malware detection has achieved certain progress, but most existing methods convert process dump to images for classification, which is still based on process byte feature classification. After the malware is loaded into memory, the original byte features will change. Compared with byte features, function call features can represent the behaviors of malware more robustly.
View Article and Find Full Text PDFReal-World Spatial Synchronization of Event-CMOS Cameras through Deep Learning: A Novel CNN-DGCNN Approach.
Sensors (Basel)
June 2024
Department of Industrial Engineering and Management, Ariel University, Ariel 40700, Israel.
This paper presents a new deep-learning architecture designed to enhance the spatial synchronization between CMOS and event cameras by harnessing their complementary characteristics. While CMOS cameras produce high-quality imagery, they struggle in rapidly changing environments-a limitation that event cameras overcome due to their superior temporal resolution and motion clarity. However, effective integration of these two technologies relies on achieving precise spatial alignment, a challenge unaddressed by current algorithms.
View Article and Find Full Text PDFGraph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.
BMC Neurosci
January 2024
PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, 100 Feet Ring Road, III Stage BSK, Dwaraka Nagar, Bengaluru, Karnataka, 560085, India.
Background: Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!