Graph Mining for Cybersecurity: A Survey
arXiv:2304.00485 · doi:10.1145/3610228
Abstract
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research.
References in corpus (32)
- Efficient Estimation of Word Representations in Vector Space
- A Comprehensive Survey on Graph Neural Networks
- DeepWalk: Online Learning of Social Representations
- LINE: Large-scale Information Network Embedding
- Graph Embedding Techniques, Applications, and Performance: A Survey
- Deep Learning for Anomaly Detection: A Survey
- struc2vec: Learning Node Representations from Structural Identity
- Cyber Security in the Age of COVID-19: A Timeline and Analysis of Cyber-Crime and Cyber-Attacks during the Pandemic
- Deep Learning for Encrypted Traffic Classification: An Overview
- Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
- Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
- graph2vec: Learning Distributed Representations of Graphs
- A Semi-supervised Graph Attentive Network for Financial Fraud Detection
- Representation Learning on Graphs: Methods and Applications
- How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
- FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
- Spam Review Detection with Graph Convolutional Networks
- Using social network analysis to prevent money laundering
- Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
- T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis
- FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
- Automatic Rumor Detection on Microblogs: A Survey
- Scalable Graph Learning for Anti-Money Laundering: A First Look
- Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods
- Identity Inference on Blockchain using Graph Neural Network
- Uncovering Insurance Fraud Conspiracy with Network Learning
- In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale
- Towards Federated Graph Learning for Collaborative Financial Crimes Detection
- Federated Graph Learning -- A Position Paper
- Heterogeneous Graph Matching Networks
- HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features
- BlockGC: A Joint Learning Framework for Account Identity Inference on Blockchain with Graph Contrast