CYGENT: A Conversational Cybersecurity Agent with Advanced Log Summarization Powered by GPT-3

Основные понятия

CYGENT, a conversational agent framework powered by GPT-3.5 turbo model, is designed to assist system administrators in ensuring optimal cybersecurity performance and resource availability by providing information, analyzing and summarizing log files, detecting events, and delivering essential instructions.

Аннотация

The paper introduces CYGENT, a conversational agent framework powered by the GPT-3.5 turbo model, designed to aid system administrators in cybersecurity tasks. The key components are:
Directory:

Chatbot - Conversational Agent

Utilizes GPT-3.5 turbo model for natural language processing and generation
Enables users to upload log files and engage in Q&A sessions for log analysis

Log Summarizer

Leverages GPT-3 models to generate human-readable summaries from complex log data
Combines data-driven and rule-based techniques for efficient summarization
Comparative analysis with CodeT5 models shows GPT-3 Davinci outperforms other LLMs

History Tab and Feedback Data Collection

Displays generated details and allows users to provide feedback
Collects user feedback to refine the models through retraining

The framework is implemented using Python, Gradio, and OpenAI libraries. Evaluation metrics like training/validation loss, accuracy, ROUGE, and BERTScore are used to assess the performance of the fine-tuned GPT-3 models. The results demonstrate the effectiveness of the CYGENT framework in enhancing log analysis and providing valuable assistance to security analysts.

Статистика

"The emergence of Large Language Models (LLMs), such as GPT (Generative Pre-trained Transformer), has revolutionized AI applications, enabling advancements in text generation, classification, and chatbot functionalities."
"By automating tasks such as log file analysis and summarization, the framework aims to improve efficiency and reduce manual effort in handling large volumes of log data."
"We fine-tuned and validated summarizer models (GPT3) using manually generated data points. Using this approach, we achieved a BERTscore of over 97%, indicating GPT-3's enhanced capability in summarizing log files into human-readable formats and providing necessary information to users."
"Our analysis consistently demonstrated that Davinci (GPT-3) model outperformed all other LLMs, showcasing higher performance."

Цитаты

"The emergence of Large Language Models (LLMs), such as GPT (Generative Pre-trained Transformer), has revolutionized AI applications, enabling advancements in text generation, classification, and chatbot functionalities."
"By automating tasks such as log file analysis and summarization, the framework aims to improve efficiency and reduce manual effort in handling large volumes of log data."
"We fine-tuned and validated summarizer models (GPT3) using manually generated data points. Using this approach, we achieved a BERTscore of over 97%, indicating GPT-3's enhanced capability in summarizing log files into human-readable formats and providing necessary information to users."
"Our analysis consistently demonstrated that Davinci (GPT-3) model outperformed all other LLMs, showcasing higher performance."

Ключевые выводы из

CYGENT

by Prasasthy Ba... в arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17160.pdf

Дополнительные вопросы

How can the CYGENT framework be further integrated with existing cybersecurity tools and systems to provide a more comprehensive security solution?

The CYGENT framework can be further integrated with existing cybersecurity tools and systems in several ways to provide a more comprehensive security solution:

Integration with SIEM (Security Information and Event Management) systems: CYGENT's log summarization capabilities can be seamlessly integrated with SIEM tools, allowing security analysts to quickly and efficiently analyze log data from various sources. This integration can enhance the overall threat detection and incident response capabilities of the security infrastructure.

Interfacing with SOAR (Security Orchestration, Automation, and Response) platforms: CYGENT's conversational agent and log analysis features can be integrated with SOAR platforms, enabling automated incident response workflows. This integration can streamline the process of incident investigation, threat mitigation, and security operations.

Collaboration with NIDS/HIDS (Network/Host-based Intrusion Detection Systems): CYGENT can be integrated with NIDS and HIDS solutions to provide real-time monitoring and analysis of system logs. This integration can enable the framework to detect anomalies, identify potential threats, and generate human-readable summaries for security teams to take appropriate actions.

Interoperability with ticketing and incident management systems: CYGENT can be integrated with ticketing and incident management systems, allowing security analysts to seamlessly create, track, and resolve security-related tickets directly from the CYGENT interface. This integration can improve the overall efficiency of the security operations workflow.

Collaboration with vulnerability management tools: CYGENT's log analysis capabilities can be leveraged to identify and prioritize vulnerabilities detected by vulnerability management tools. This integration can enhance the overall vulnerability management process and help security teams focus on the most critical issues.

Integration with threat intelligence platforms: CYGENT can be connected to threat intelligence platforms, allowing it to access and leverage up-to-date threat data. This integration can enhance CYGENT's ability to detect and respond to emerging threats, providing a more comprehensive security solution.

By integrating CYGENT with these existing cybersecurity tools and systems, the framework can become a powerful hub for security operations, providing a centralized and streamlined approach to managing security-related tasks and enhancing the overall security posture of the organization.

What are the potential limitations or challenges in deploying the CYGENT framework in real-world enterprise environments, and how can they be addressed?

Potential limitations or challenges in deploying the CYGENT framework in real-world enterprise environments include:

Data privacy and security concerns: Enterprise environments often have strict data privacy and security requirements. Integrating CYGENT with sensitive data sources may raise concerns about data protection and compliance. To address this, CYGENT can be designed with robust data security measures, such as encryption, access controls, and secure data transfer protocols, to ensure the confidentiality and integrity of the data.

Integration with legacy systems: Many enterprise environments rely on legacy systems and technologies, which may pose challenges in seamlessly integrating CYGENT. To overcome this, CYGENT can be designed with a modular and flexible architecture, allowing for easy integration with various data sources and systems through well-defined APIs and connectors.

Scalability and performance: As the number of users and the volume of data increase in enterprise environments, CYGENT must be able to handle the increased load without compromising performance. This can be addressed by implementing scalable infrastructure, such as distributed computing and cloud-based solutions, and optimizing the system's architecture for efficient data processing and response times.

Customization and adaptability: Enterprise environments often have unique security requirements and workflows. CYGENT must be highly customizable and adaptable to meet the specific needs of each organization. This can be achieved by providing a flexible configuration management system, allowing security teams to tailor the framework to their specific requirements.

User adoption and training: Successful deployment of CYGENT in enterprise environments requires user adoption and training. Security teams and other stakeholders must be educated on the capabilities and benefits of the framework to ensure its effective utilization. This can be addressed by providing comprehensive documentation, training materials, and ongoing support to facilitate the adoption of CYGENT.

Maintenance and updates: As cybersecurity threats and technologies evolve, CYGENT must be regularly maintained and updated to ensure its continued effectiveness. This can be addressed by implementing a robust update and maintenance strategy, including automated update mechanisms and clear communication channels with users.

By addressing these potential limitations and challenges, the CYGENT framework can be successfully deployed in real-world enterprise environments, providing a comprehensive and adaptable security solution that meets the unique requirements of each organization.

How can the CYGENT framework be extended to provide predictive analytics or proactive threat detection capabilities by leveraging the power of Large Language Models?

The CYGENT framework can be extended to provide predictive analytics and proactive threat detection capabilities by leveraging the power of Large Language Models (LLMs) in the following ways:

Anomaly detection and predictive analytics: The CYGENT framework can be enhanced by fine-tuning LLMs, such as GPT-3, to detect anomalies and patterns in log data that may indicate potential security threats. By training the LLMs on historical security data and known threat patterns, CYGENT can develop the ability to predict and identify emerging threats before they materialize, enabling proactive security measures.

Threat intelligence generation: LLMs can be leveraged to analyze various data sources, including security reports, threat intelligence feeds, and open-source intelligence, to generate comprehensive threat intelligence. This intelligence can be integrated into the CYGENT framework, allowing it to provide security teams with up-to-date information on emerging threats, vulnerabilities, and attack vectors.

Automated incident response: By combining LLM-powered predictive analytics and threat intelligence, the CYGENT framework can be extended to automate incident response workflows. The system can generate detailed incident reports, recommend appropriate mitigation strategies, and even trigger automated actions, such as network quarantine or system patching, to address identified threats in a timely and efficient manner.

Natural language-based security queries: LLMs can be leveraged to enhance the conversational capabilities of the CYGENT framework, allowing security analysts to interact with the system using natural language. This can enable users to ask complex security-related questions, receive detailed responses, and even generate custom reports or analyses based on their queries.

Proactive risk assessment: CYGENT can utilize LLMs to perform proactive risk assessments by analyzing various data sources, including network traffic, user behavior, and system configurations. This can help identify potential vulnerabilities, misconfigurations, and areas of concern, allowing security teams to address them before they can be exploited by threat actors.

Automated security policy generation: LLMs can be trained to understand the context and requirements of an organization's security policies. CYGENT can then leverage this knowledge to generate and recommend tailored security policies, access controls, and other security measures based on the organization's specific needs and risk profile.

By integrating these LLM-powered capabilities, the CYGENT framework can evolve into a comprehensive security solution that not only provides reactive security measures but also proactively identifies and mitigates threats, empowering security teams to stay ahead of the curve in the ever-changing cybersecurity landscape.