بررسی یک روش ترکیبی جدید سیستم تشخیص نفوذ بر روی مجموعه داده های مختلف

نوع مقاله : مقاله پژوهشی

نویسنده

پردیس صنعتی شهدای هویزه، دانشگاه شهید چمران اهواز، اهواز، ایران.

چکیده

تشخیص نفوذ یک مسئله طبقه‌بندی است که در آن روش‌های مختلف یادگیری ماشین (ML) و داده‌کاوی (DM) برای طبقه‌بندی داده‌های شبکه در ترافیک عادی و حمله استفاده می‌شود. علاوه بر این، انواع حملات شبکه در طول سال‌ها تغییر کرد. در این مقاله سعی شد دو مدل از سیستم‌های تشخیص نفوذ، باهم مقایسه شود، که این مدل‌ها شامل، شبکه استنتاج عصبی-فازی سازگار (ANFIS) و ماشین‌های بردار پشتیبان (SVM) می‌باشند. علاوه بر این چندین نمونه از مجموعه داده‌های مربوط به سیستم‌های تشخیص نفوذ را موردبررسی و ارزیابی قرار می‌دهد. در ادامه، یک روش ترکیبی جدید را بیان می‌کند که از بهینه‌سازی ازدحام ذرات (PSO) به‌منظور ایجاد ترکیب دسته‌بندها برای ایجاد دقت بهتر برای تشخیص نفوذ، استفاده کرده است. نتایج آزمایش نشان می‌دهد که روش جدید می‌تواند کارایی بهتری بر اساس معیارهای مختلف ارزیابی، ارائه کند. این مقاله مجموعه داده‌های مختلف را برای ارزیابی مدل IDS فهرست می‌کند و کارایی روش ترکیبی پیشنهادی بر مجموعه داده‌های IDS را موردبحث قرار می‌دهد که می‌تواند برای استفاده از مجموعه داده‌ها برای توسعه IDS مبتنی بر ML و DM کارآمد و مؤثر بوده و مورداستفاده قرار گیرد.

کلیدواژه‌ها


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دوره 10، شماره 3 - شماره پیاپی 39
شماره پیاپی 39، فصلنامه پاییز
دی 1401
صفحه 43-57
  • تاریخ دریافت: 19 مهر 1400
  • تاریخ بازنگری: 18 آذر 1400
  • تاریخ پذیرش: 18 مرداد 1401
  • تاریخ انتشار: 01 دی 1401