Python
Aim : The aim of this project is to design and develop a scalable cyber threat intelligence system that analyzes dark web content to identify potential cyber threats such as hacks, malware, and vulnerabilities, using API–powered large language models for efficient and high-speed reasoning.
Abstract : The dark web hosts a significant volume of cyber threat–related discussions that often precede real-world cyberattacks. Traditional cyber threat intelligence systems primarily focus on surface web sources and rely on monolithic or rule-based approaches that struggle with complex, multilingual, and informal dark web content. In this work, we propose cyber threat intelligence framework inspired by existing research, but redesigned to utilize latest dataset and new API–based large language models. The proposed system decomposes complex CTI tasks into specialized agents responsible for translation, contextual analysis, relevancy detection, and threat categorization. By leveraging high-performance inference capabilities, the system aims to provide faster, scalable, and accurate threat intelligence while maintaining modularity and adaptability. This framework enables proactive identification of emerging threats with minimal human intervention.
Proposed System
The proposed system designed to analyze dark web content efficiently and accurately. The system decomposes the CTI pipeline into specialized agents, each performing a focused task. Instead of relying on a single LLM, agents collaborate sequentially and share contextual insights to derive actionable intelligence.
API–based LLMs are used to achieve low-latency inference and high throughput, enabling real-time or near–real-time analysis. The framework is modular, extensible, and adaptable to different threat analysis requirements.
Advantages
- Modular multi-agent architecture improves accuracy and interpretability
- Faster inference and reduced latency using Latest API models
- Scalable design suitable for large volumes of dark web data
- Improved contextual understanding of human conversations
- Reduced human intervention through autonomous agent collaboration
- Flexible integration of additional agents in future
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