Automatic Collection and Analysis System for Cyber Threat Intelligence Based on Large Language Models

This open-source project uses large language models (LLMs) to automatically collect and analyze cyber threat intelligence (CTI) from public data sources. It aims to address the issues of low efficiency and easy omission in traditional manual screening and analysis, demonstrating the practical application potential of AI in the cybersecurity field. The core of the project is to convert unstructured security text into structured intelligence, improving the speed of threat detection and response, and providing support for enterprise defense and security decision-making.

Project Background and Significance

In today's digital age, cybersecurity threats are becoming increasingly complex and ever-changing. Traditional threat intelligence collection methods often rely on manual screening and analysis of massive security data, which is inefficient and prone to missing key information. With the rapid development of large language model technology, applying it to automated collection and analysis of threat intelligence has become an important research direction in the cybersecurity field.

Cyber Threat Intelligence (CTI) refers to evidence-based knowledge about existing or emerging threats, including information such as attackers' capabilities, intentions, opportunities, and Indicators of Compromise (IoC). Timely acquisition of accurate threat intelligence is crucial for enterprises to defend against cyberattacks and protect sensitive data.

Technical Architecture and Core Mechanisms

Data Collection Layer

The system first obtains raw information from multiple public security data sources, which may include:

• Threat announcements and research reports from security vendors
• Update information from the CVE vulnerability database
• Discussion content from security communities and forums
• Security alerts issued by government CERTs

Large Language Model Processing Layer

The collected raw text is processed using large language models. LLMs perform several key tasks in this system:

1. Entity Recognition: Identify threat-related entities from text, such as malicious IP addresses, domain names, file hashes, and attack organization names.

2. Relationship Extraction: Analyze the relationships between threat entities, such as an attack organization using specific malware, or a certain vulnerability being used in specific attack activities.

3. Event Summarization: Condense lengthy security reports into structured threat event summaries, extracting key information such as attack timelines, impact ranges, and defense recommendations.

4. Intelligence Classification: Automatically classify intelligence according to threat types (e.g., phishing, ransomware, APT attacks) to facilitate subsequent retrieval and correlation analysis.

Intelligence Storage and Retrieval

The processed structured threat intelligence is stored in a database, supporting efficient querying and correlation analysis. The system can track the evolution trajectory of specific threats, identify attack patterns, and provide data support for security decision-making.

Practical Application Scenarios

The system has a wide range of application scenarios:

Enterprise Security Operations Center (SOC): Security analysts can use the system to quickly understand the latest threat situation, timely obtain high-risk vulnerability information targeting their industry, and deploy protective measures in advance.

Threat Hunting: By analyzing the threat intelligence collected by the system, security teams can proactively search for possible intrusion signs in the network, realizing the transformation from passive response to active defense.

Incident Response: When a security incident occurs, the system can quickly provide relevant threat background information, helping analysts determine the attack source, motivation, and possible follow-up actions.

Compliance Reporting: Automatically generated threat intelligence summaries can be used to report the security situation to management and regulatory authorities, meeting compliance requirements.

Technical Advantages and Challenges

Technical Advantages

• High Automation: Significantly reduces the workload of manually reading and organizing security reports
• Fast Processing Speed: Can analyze large amounts of text data in a short time
• Strong Adaptability: Large language models can handle security information from various formats and sources
• Knowledge Integration: Can correlate and integrate threat information scattered across different sources

Challenges

• Data Accuracy: Need to verify whether the intelligence extracted by the model is accurate and reliable
• Timeliness: The value of threat intelligence decays rapidly over time, requiring timely updates
• False Positive Control: Need to balance the coverage of intelligence and the false positive rate
• Privacy Compliance: Need to pay attention to complying with relevant regulations when collecting public data

Future Development Directions

With the continuous progress of large language model technology, such systems are expected to further develop in the following aspects:

1. Multimodal Intelligence Analysis: Integrate non-text threat intelligence sources such as images and videos
2. Real-time Intelligence Stream Processing: Support real-time analysis and early warning of security event streams
3. Multilingual Support: Process security information from around the world
4. Intelligence Sharing: Support the exchange of threat intelligence with other organizations' security platforms

Summary and Insights

The threat intelligence collection system based on large language models represents an important application direction of AI in the cybersecurity field. It not only demonstrates the possibility of technological innovation but also provides new ideas for solving practical security problems. For security practitioners, mastering how to combine large language models with traditional security technologies will become an important skill for future career development.

The open-source nature of the project also means that the security community can participate in improvements together, promoting the overall progress of automated threat intelligence processing technology.