DNS-GT: A Graph-based Transformer Approach to Learn Embeddings of Domain Names from DNS Queries
DNS-GT: A Graph-based Transformer Approach to Learn Embeddings of Domain Names from DNS Queries explores DNS-GT leverages a Transformer-based model to enhance domain name embeddings for improved network intrusion detection.. Commercial viability score: 8/10 in Cybersecurity.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisA Boltzmann-machine-enhanced Transformer For DNA Sequence ClassificationViability: 3.0
- Builds On ThisMulti-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud DetectionViability: 7.0
- Builds On ThisBeyond the Class Subspace: Teacher-Guided Training for Reliable Out-of-Distribution Detection in Single-Domain ModelsViability: 5.0
- Builds On ThisRevisiting Cross-Attention Mechanisms: Leveraging Beneficial Noise for Domain-Adaptive LearningViability: 7.0
- Builds On ThisAre LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?