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HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs) (2309.16021v1)

Published 27 Sep 2023 in cs.CR

Abstract: Machine learning (ML) is crucial in network anomaly detection for proactive threat hunting, reducing detection and response times significantly. However, challenges in model training, maintenance, and frequent false positives impact its acceptance and reliability. Explainable AI (XAI) attempts to mitigate these issues, allowing cybersecurity teams to assess AI-generated alerts with confidence, but has seen limited acceptance from incident responders. LLMs present a solution through discerning patterns in extensive information and adapting to different functional requirements. We present HuntGPT, a specialized intrusion detection dashboard applying a Random Forest classifier using the KDD99 dataset, integrating XAI frameworks like SHAP and Lime for user-friendly and intuitive model interaction, and combined with a GPT-3.5 Turbo, it delivers threats in an understandable format. The paper delves into the system's architecture, components, and technical accuracy, assessed through Certified Information Security Manager (CISM) Practice Exams, evaluating response quality across six metrics. The results demonstrate that conversational agents, supported by LLM and integrated with XAI, provide robust, explainable, and actionable AI solutions in intrusion detection, enhancing user understanding and interactive experience.

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Citations (23)
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Summary

  • The paper introduces HuntGPT, integrating ML-based anomaly detection, explainable AI, and LLMs to enhance cybersecurity threat analysis.
  • It employs a Random Forest classifier on the KDD99 dataset along with SHAP and Lime frameworks to improve decision interpretability.
  • Evaluation results show GPT-3.5 Turbo achieved 72%-82.5% success on cybersecurity exams, underscoring its potential for real-time threat response.

Integration of Machine Learning-Based Anomaly Detection and Explainable AI with LLMs in Cybersecurity

Introduction

The rapid increase in cyber-attacks has necessitated the development of more efficient cybersecurity strategies. Amidst this need, the integration of Machine Learning (ML) methods for anomaly detection has become increasingly prevalent. However, the complexity of ML models and the occurrence of false positives have posed challenges, undermining their trustworthiness and acceptability. This has led to the emergence of Explainable Artificial Intelligence (XAI) techniques aimed at making AI decisions more understandable to analysts and model maintainers. Against this backdrop, the paper introduces HuntGPT, a prototype that combines anomaly detection, XAI, and conversational AI powered by LLMs to enhance cybersecurity operations.

System Architecture and Development

HuntGPT is architected to provide a cohesive and user-friendly interface for cybersecurity operations. The system capitalizes on a Random Forest classifier for anomaly detection, trained on the KDD99 dataset, and utilizes XAI frameworks such as SHAP and Lime to enhance interpretability. Furthermore, it incorporates a conversational agent using OpenAI's GPT-3.5 Turbo, facilitating interactive and understandable communication of detected threats.

The system is structured into three layers: the analytics engine for network packet analysis, data storage utilizing Elasticsearch for information organization, and a user interface developed with Gradio for interactive user engagement. This layered approach ensures modular development, maintenance enhancement, and adaptability to evolving requirements.

Evaluation and Results

Evaluation of the HuntGPT prototype focused on technical accuracy and response readability, employing certified cybersecurity exams and user experience feedback. The system demonstrated considerable competence in cybersecurity, with the GPT-3.5 Turbo model achieving success rates between 72% and 82.5% across various standardized cybersecurity exams. Readability analysis of the conversational agent's responses revealed a graduate-level comprehension requirement, suggesting a need for some degree of specialized knowledge for optimal interaction.

Implications and Future Directions

The paper's findings indicate that integrating LLM-based conversational agents and XAI in cybersecurity can improve the comprehensibility and user-friendliness of anomaly detection systems. The successful implementation of HuntGPT highlights the potential for such integrated systems in enhancing cybersecurity operations' efficiency and efficacy.

Looking forward, the research suggests avenues for improving ML model accuracy, incorporating real-time threat detection, and enhancing the conversational agent's capability to issue active commands to cybersecurity management systems. These enhancements aim at real-time, actionable responses to security threats, representing a significant advancement in the field of cybersecurity operations.

Conclusion

The integration of ML-based anomaly detection, XAI, and conversational AI presents a promising avenue for advancing cybersecurity operations. The HuntGPT system exemplifies the potential of such integrations in providing explainable, actionable, and user-friendly cybersecurity solutions. Future research will focus on refining these technologies to meet the evolving challenges of cybersecurity threat detection and response.

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