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Ensuring compliance and finding anomalies in large log collections are major issues in modern cybersecurity environments. This study presents LogGPT, a novel approach that revolutionizes the conventional paradigm of compliance detection and monitoring. Large Language Models (LLMs) are a powerful tool that LogGPT uses to analyze logs without the assistance of other programs. The study examines the inherent drawbacks of the current approaches and offers LogGPT as an advanced substitute. By showcasing LLMs’ greater flexibility and contextual awareness compared to more conventional deep learning models, LogGPT offers improved compliance monitoring accuracy by 14% over models like GPT4ALL, and overall, it achieves an accuracy of 89.43%. Experimental results show the effectiveness of the proposed approach.

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Enhancing Cybersecurity Log Analysis with LogGPT: A Novel LLM-Based Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Raj Laxmi Singh,
  • Vaishnavi Jadhav,
  • Mahak Chamria,
  • Vikas Kumar Jain

摘要

Ensuring compliance and finding anomalies in large log collections are major issues in modern cybersecurity environments. This study presents LogGPT, a novel approach that revolutionizes the conventional paradigm of compliance detection and monitoring. Large Language Models (LLMs) are a powerful tool that LogGPT uses to analyze logs without the assistance of other programs. The study examines the inherent drawbacks of the current approaches and offers LogGPT as an advanced substitute. By showcasing LLMs’ greater flexibility and contextual awareness compared to more conventional deep learning models, LogGPT offers improved compliance monitoring accuracy by 14% over models like GPT4ALL, and overall, it achieves an accuracy of 89.43%. Experimental results show the effectiveness of the proposed approach.