Evaluation of the LLM’s in Cyber Threat Intelligence

In the dynamic field of cybersecurity, the ability to quickly identify and respond to threats is crucial. LLMs such as GPT-3 are revolutionizing this field thanks to their advanced natural language processing capabilities. A fundamental aspect of understanding their effectiveness is the use of benchmarks, which allow us to evaluate and compare their performance in various Cyber Threat Intelligence (CTI) tasks.


What is Benchmarking in CTI?

Benchmarking involves evaluating and comparing the performance of different LLMs using specific CTI data sets. This helps us understand which models are most effective in threat detection and analysis.


Study Methodology

Studies use real data sets of security incidents and cyber threats. The LLMs evaluated include popular models such as GPT-3 and BERT, and standard metrics such as accuracy, recall, and F1-score are used to measure their performance. Tasks such as threat type classification, attack pattern detection, and detailed incident report generation were analyzed.


Benchmarking Results

The results of these studies show that LLMs outperform traditional methods in identifying threats and generating relevant responses.

  • Code Vulnerability Detection
    GPT-3 was evaluated on a dataset of Python code fragments labeled with and without vulnerabilities. It achieved an accuracy of 85%, outperforming several traditional models in vulnerability detection.
  • Threat Classification
    In the task of classifying cyber threat types based on textual descriptions, LLMs achieved 90% accuracy, demonstrating their ability to correctly identify threats such as phishing, malware and DDoS attacks.
  • Incident Report Generation
    The LLMs generated detailed reports that were evaluated by cybersecurity experts. The accuracy and relevance of these reports surpassed those generated by conventional methods, highlighting the LLMs’ ability to provide in-depth analysis and useful recommendations.

Challenges and Proposed Improvements

Despite their good performance, LLMs face challenges such as bias management and the need for specific adjustments. Some of the proposals to improve their effectiveness include:

  • Fine Tuning with CTI-Specific Data:
    Training models with more cybersecurity domain-specific data sets can improve their accuracy and relevance.
  • Integration with Other Security Tools:
    Combining LLMs with traditional security tools can boost detection and response capabilities.

Practical Applications of LLMs in CTI

Large Scale Language Models can be used in different tasks within Cyber Threat Intelligence. Here are some practical examples:

  • Threat Identification:
    LLMs can analyze large volumes of data for threat patterns, identifying potential incidents before they occur. For example, in detecting anomalies in access logs, LLMs identified suspicious patterns indicating unauthorized access attempts.
  • Incident Classification:
    LLMs can accurately categorize security incidents, helping to prioritize responses based on severity. In tests, LLMs correctly classified phishing and malware incidents in real time.
  • Report Generation:
    LLMs can generate detailed incident reports, providing in-depth analysis and recommendations for threat mitigation. A case study showed how GPT-3 generated a report of a ransomware attack with detailed steps for recovery and future prevention.

For those interested in a more in-depth study on this topic, please refer to, “SEVENLLM : Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence” or view and download it below. This study provides detailed analysis and comprehensive benchmarks on the performance of LLMs in Cyber Threat Intelligence.

LLMs are transforming cyber threat intelligence, offering new capabilities for threat detection and analysis. As we continue to fine-tune and improve these models, we can expect even greater advances in cyber threat protection.


Izan Franco Moreno