Enhancing Cybersecurity Incident Analysis and Response Capabilities through Large Language Models: The SEVENLLM Framework

Основні поняття

A framework called SEVENLLM is introduced to benchmark, elicit, and improve the abilities of large language models (LLMs) in analyzing and responding to cybersecurity incidents.

Анотація

The paper presents the SEVENLLM framework, which aims to enhance the capabilities of large language models (LLMs) in analyzing and responding to cybersecurity incidents, referred to as security events.
Key highlights:

Data Collection and Preprocessing:

Curated a high-quality bilingual (English and Chinese) corpus of over 10,000 cybersecurity incident reports from leading security vendors and media sources.
Preprocessed the data to extract relevant text-based information and remove low-quality content.

SEVENLLM-Instruct:

Designed a pipeline to automatically select tasks from a task pool and convert raw cybersecurity texts into supervised question-answer pairs.
The task pool consists of 13 understanding tasks (e.g., entity recognition, relationship extraction) and 15 generation tasks (e.g., vulnerability analysis, incident response planning).
Used the "Select-Instruct" method to create the SEVENLLM-Instruct dataset, which serves as the instruction corpus for fine-tuning LLMs.

SEVENLLM Model:

Fine-tuned open-source LLMs (e.g., Llama, Qwen) on the SEVENLLM-Instruct dataset using multi-task learning objectives tailored for cyber threat intelligence (CTI).
The fine-tuned SEVENLLM models aim to streamline the analysis process and reduce the reliance on human expertise, thereby accelerating and enhancing the capabilities of analysts in threat identification and response.

SEVENLLM-Bench:

Constructed a comprehensive evaluation benchmark, SEVENLLM-Bench, to assess the performance of LLMs in CTI tasks.
The benchmark includes multiple-choice questions and generation tasks to thoroughly evaluate the understanding and generation capabilities of LLMs in the cybersecurity domain.

The SEVENLLM framework and the associated datasets and benchmark contribute to bridging the gap between the general language understanding and generation capabilities of LLMs and the specialized requirements of the cybersecurity field.

Статистика

Over 10 billion cybersecurity incidents globally
6,706 English and 1,779 Chinese high-quality cybersecurity incident reports
SEVENLLM-Instruct dataset contains nearly 85,000 samples
SEVENLLM-Bench dataset contains 1,200 test samples

Цитати

"To address the increasing complexity and frequency of cybersecurity incidents emphasized by the recent cybersecurity threat reports with over 10 billion instances, cyber threat intelligence (CTI) plays a critical role in the modern cybersecurity landscape by offering the insights required to understand and combat the constantly evolving nature of cyber threats."
"Inspired by the powerful capability of large language models (LLMs) in handling complex tasks, in this paper, we introduce a framework to benchmark, elicit, and improve cybersecurity incident analysis and response abilities in LLMs for Security Events (called SEVENLLM)."

Ключові висновки, отримані з

SEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence

by Hangyuan Ji,... о arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03446.pdf

Глибші Запити

How can the SEVENLLM framework be extended to incorporate real-time cybersecurity data feeds and provide dynamic threat analysis and response recommendations?

To incorporate real-time cybersecurity data feeds into the SEVENLLM framework for dynamic threat analysis and response recommendations, several key steps can be taken:

Data Integration: Develop a system that can ingest real-time data feeds from various sources such as security logs, network traffic, threat intelligence feeds, and security alerts. This data should be processed and integrated into the SEVENLLM platform.

Streaming Analytics: Implement streaming analytics capabilities to process and analyze the incoming data in real-time. This involves continuously monitoring the data streams for anomalies, patterns, and potential threats.

Dynamic Task Generation: Enhance the SEVENLLM framework to dynamically generate tasks based on the real-time data inputs. These tasks should be tailored to the specific cybersecurity incidents and threats identified from the data feeds.

Adaptive Learning: Implement adaptive learning algorithms that can update the model based on the new data inputs. This will allow SEVENLLM to continuously learn and improve its threat analysis and response capabilities over time.

Automated Response: Integrate automated response mechanisms into the framework to enable quick and effective responses to identified threats. This could include automated incident response actions, alerts, and mitigation strategies.

By incorporating these elements, the SEVENLLM framework can evolve into a real-time cybersecurity analysis and response system that can effectively handle the dynamic nature of cyber threats.

What are the potential limitations of using LLMs for cybersecurity tasks, and how can these be addressed to ensure the reliability and trustworthiness of the system?

While LLMs offer significant capabilities for cybersecurity tasks, there are several potential limitations that need to be addressed to ensure the reliability and trustworthiness of the system:

Bias and Interpretability: LLMs may exhibit biases in their predictions and lack interpretability, making it challenging to understand how decisions are made. Addressing this requires transparency in model training, bias detection, and interpretability techniques.

Data Privacy and Security: LLMs trained on sensitive cybersecurity data may pose risks to data privacy and security. Implementing robust data protection measures, such as encryption and access controls, is crucial to mitigate these risks.

Adversarial Attacks: LLMs are vulnerable to adversarial attacks, where malicious inputs can manipulate model outputs. Robust defenses, such as adversarial training and input validation, can help mitigate these attacks.

Domain Specificity: Generic LLMs may lack domain-specific knowledge required for cybersecurity tasks. Fine-tuning models on cybersecurity-specific data and tasks can enhance their performance in this domain.

Continual Learning: LLMs may struggle with continual learning and adapting to new threats. Implementing mechanisms for continual learning and model updating can help the system stay current with emerging threats.

By addressing these limitations through a combination of technical solutions, robust governance frameworks, and ongoing monitoring and evaluation, the reliability and trustworthiness of LLM-based cybersecurity systems can be enhanced.

Given the rapidly evolving nature of cybersecurity threats, how can the SEVENLLM framework be adapted to continuously learn and update its knowledge base to stay ahead of emerging threats?

To ensure that the SEVENLLM framework can continuously learn and update its knowledge base to stay ahead of emerging threats, the following strategies can be implemented:

Active Monitoring: Implement a system for active monitoring of cybersecurity trends, threat intelligence reports, and emerging attack vectors. This will enable SEVENLLM to stay informed about the latest threats.

Automated Data Collection: Set up automated processes to collect and ingest new cybersecurity data sources, including threat feeds, incident reports, and security alerts. This data should be used to update the model regularly.

Incremental Learning: Implement incremental learning techniques that allow SEVENLLM to update its knowledge base with new information without retraining the entire model. This ensures that the system can adapt quickly to new threats.

Feedback Loops: Establish feedback loops that capture the performance of SEVENLLM in real-world scenarios. This feedback can be used to identify areas for improvement and guide the model updating process.

Collaboration with Experts: Foster collaboration with cybersecurity experts to provide domain-specific insights and validate the system's responses. Expert feedback can help refine the model and ensure its relevance to current threats.

Regular Model Evaluation: Conduct regular evaluations of the SEVENLLM framework to assess its performance against evolving threats. This includes testing the model on new datasets, scenarios, and attack vectors to ensure its effectiveness.

By incorporating these strategies into the SEVENLLM framework, the system can continuously learn and adapt to emerging cybersecurity threats, staying proactive in its threat analysis and response capabilities.