NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification
arXiv:2103.03939 · doi:10.1145/3468791.3468814
Abstract
Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially. While some existing malware detection and classification approaches successfully leverage network traffic data, they treat network flows between pairs of endpoints independently and thus fail to leverage rich communication patterns present in the complete network. Our approach first extracts flow graphs and subsequently classifies them using a novel edge feature-based graph neural network model. We present three variants of our base model, which support malware detection and classification in supervised and unsupervised settings. We evaluate our approach on flow graphs that we extract from a recently published dataset for mobile malware detection that addresses several issues with previously available datasets. Experiments on four different prediction tasks consistently demonstrate the advantages of our approach and show that our graph neural network model can boost detection performance by a significant margin.
References in corpus (11)
- A Comprehensive Survey on Graph Neural Networks
- Semi-Supervised Classification with Graph Convolutional Networks
- Neural Message Passing for Quantum Chemistry
- Geometric deep learning: going beyond Euclidean data
- Deep Learning for Anomaly Detection: A Survey
- Simplifying Graph Convolutional Networks
- Representation Learning on Graphs with Jumping Knowledge Networks
- Neural Relational Inference for Interacting Systems
- Edge Attention-based Multi-Relational Graph Convolutional Networks
- ANAE: Learning Node Context Representation for Attributed Network Embedding
- PushNet: Efficient and Adaptive Neural Message Passing