.png){kind=icon}
.png){kind=icon}
Return
Cyberattack Graph Modeling for Visual Analytics
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3304640.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cybersecurity research demands continuous monitoring of the dynamic threat landscape to detect novel attacks. Researchers and security professionals often deploy honeypot networks to intercept and examine real attack data. However, due to the volume and variety of the collected data, it is very challenging for security analysts to investigate the attacks, compare their characteristics and infer their potential connections. To this end, we propose a novel graph-based cyberattack model for storing, analyzing, and visualizing honeynet-captured attacks as the main contribution of our work. Our model enables attack graph analysis and presents the attack data analogous to the Cyber Kill Chain framework to enable intuitive visualizations. We construct the attack graph by decomposing the intercepted attacks into a set of unique entities (represented as nodes) and actions (represented as edges) and merge them into a global attack graph. We develop a user-centric, interactive attack analysis and visualization tool that leverages the proposed model to aid the heuristic cyberattack investigation. We describe the design and technical implementation of the developed model and visual-interactive tool in detail. Finally, we demonstrate the developed tools and validate the model in an analysis of real-world attack data captured on our own distributed honeypot platform. We use the attack model and (sub)graph visualizations to depict attack topologies, identify recurring attackers, and quantify detected malware types. We also leverage graph data science algorithms to uncover and rank malware distribution networks, reveal hidden links between the attackers, and cluster the attack entities to identify potential botnets.
Keywords:
Data visualization
Analytical models
Cyberattack
Data models
Visual analytics
Monitoring
Risk management
Big Data
Botnet
Computer security
Data science
Malware
Attack modeling
attacker links
big data
botnet detection
cyberattack
cybersecurity
cyberthreat intelligence
graph data science
graph modeling
honeynet
honeypot
malware distribution networks
threat modeling
visual analytics
visualization
cyberattack analysis
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W