BejaGNN: behavior-based Java malware detection via graph neural network
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
BejaGNN: behavior-based Java malware detection via graph neural network
Authors
Keywords
-
Journal
JOURNAL OF SUPERCOMPUTING
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-04-18
DOI
10.1007/s11227-023-05243-x
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- EfficientNet convolutional neural networks-based Android malware detection
- (2022) Pooja Yadav et al. COMPUTERS & SECURITY
- Malware classification with Word2Vec, HMM2Vec, BERT, and ELMo
- (2022) Aparna Sunil Kale et al. Journal of Computer Virology and Hacking Techniques
- Self-Supervised Learning of Graph Neural Networks: A Unified Review
- (2022) Yaochen Xie et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- GDroid: Android malware detection and classification with graph convolutional network
- (2021) Han Gao et al. COMPUTERS & SECURITY
- Towards Next-Generation Cybersecurity with Graph AI
- (2021) Benjamin Bowman et al. Operating Systems Review (ACM)
- A novel framework for image-based malware detection with a deep neural network
- (2021) Yifei Jian et al. COMPUTERS & SECURITY
- Jadeite: A novel image-behavior-based approach for Java malware detection using deep learning
- (2021) Islam Obaidat et al. COMPUTERS & SECURITY
- S3Feature: A static sensitive subgraph-based feature for android malware detection
- (2021) Fan Ou et al. COMPUTERS & SECURITY
- IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture
- (2020) Danish Vasan et al. Computer Networks
- Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image
- (2020) Minkyoung CHO et al. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
- Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features
- (2020) Maryam Nisa et al. Applied Sciences-Basel
- A Survey of Android Malware Detection with Deep Neural Models
- (2020) Junyang Qiu et al. ACM COMPUTING SURVEYS
- Malicious code detection based on CNNs and multi-objective algorithm
- (2019) Zhihua Cui et al. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
- Deep learning at the shallow end: Malware classification for non-domain experts
- (2018) Quan Le et al. Digital Investigation
- A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
- (2018) HongYun Cai et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- FlowDroid
- (2014) Steven Arzt et al. ACM SIGPLAN NOTICES
- Deriving common malware behavior through graph clustering
- (2013) Younghee Park et al. COMPUTERS & SECURITY
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now