بهبود نحوه تجزیه و تحلیل داده های حجیم مربوط به فایل لاگ با استفاده از مدل زبان بزرگ LLG
محورهای موضوعی : شبکه های عصبی و یادگیری عمیق
بابک نیکمرد
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گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، دولت آباد، ایران
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آذین پیشداد
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گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، دولت آباد، ایران
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گلناز آقایی قزوینی
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گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، دولت آباد، ایران
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مهرداد عباسی
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گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، دولت آباد، ایران
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کلید واژه: شبکه عصبی, هوش مصنوعی مولد, مدل زبان بزرگ, فایل لاگ,
چکیده مقاله :
هر روز، سازمان‌ها حجم قابل‌توجهی از فایل‌های رخداد (لاگ) تولید می‌کنند که برای بررسی شرایط، اشکال‌زدایی و رفع ناهنجاری‌ها نیاز به پردازش دارند. برون سپاری چنین فرایندی به دلیل نیاز به پردازش بلادرنگ و نگهداری امنیتی مناسب نیست. با توجه به انبوه نرم افزارها و سرویس‌های مختلف، سازمان‌ها با حجم قابل توجهی از گزارش‌ها و رخدادهای تولیدی مواجه هستند که به جای حذف یا نادیده گرفته شدن، باید پردازش شوند. در روش سنتی، کارشناسان روزانه به صورت دستی پرونده‌های رخداد را بررسی می‌کنند که این امر از یک سو باعث کندی فرآیند، افزایش زمان و عدم دقت و از سوی دیگر به دلیل نیاز به نیروی متخصص، هزینه‌های بالای استخدام را در پی دارد. این مقاله راه حلی را معرفی می‌کند که از شبکه‌های عصبی مولد برای ایجاد یک ساختار محلی برای تجزیه و تحلیل گزارش در سازمان استفاده می‌شود. این فرآیند شامل بازیابی و تجزیه فایل‌های متنی از بخش‌های مختلف، تقسیم آن‌ها به بخش‌های قابل مدیریت، جاسازی و ذخیره آن‌ها در یک پایگاه داده برداری است. در این ساختار، یک فرد آموزش دیده بدون تخصص خاص می‌تواند به سرعت به اطلاعات لازم با استفاده از اعلان‌های مناسب (پرامپت نویسی) از یک مدل زبان بزرگ که به صورت محلی در سازمان توسعه یافته و در هر زمان قابل دسترسی است، استفاده کند. ازهمین روی، روش پیشنهادی می¬تواند باعث پایداری امنیت، افزایش سرعت تجزیه و تحلیل و کاهش هزینه‌های منابع انسانی شود.
Nowdays, organizations generate a significant volume of log files that require processing for condition checking, debugging, and anomaly resolution. Outsourcing such processing is not suitable due to the need for real-time processing and security maintenance. Given the multitude of different software and services, organizations face a substantial volume of production logs that should be processed rather than deleted or ignored. In the traditional approach, experts manually check the logs daily. This, on one hand, slows down the process, increases the time and inaccuracy, and, on the other hand, results in a high hiring cost due to the need for an expert force. This article introduces a solution that employs generative neural networks to establish a local structure for log analysis within the organization. The process involves retrieving and parsing text files from various sectors, segmenting them into manageable portions, embedding them, and storing them in a vector database. In this structure, a trained individual without special expertise can quickly access necessary information using appropriate prompts from a local language model available at any time. As a result, three overarching goals are achieved: maintaining security, increasing the speed of analysis, and reducing human resource costs.
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