نظام ذكي لكشف التطفل باستخدام التعلم العميق والاندماج متعدد الوسائط مع تصنيف الغابات العشوائية
DOI:
https://doi.org/10.54153/sjpas.2026.v8i1.1286الكلمات المفتاحية:
IDS، , CNN، , DL، , ML، , Multimodal، , RFالملخص
ازداد الطلب المُلِحّ على أنظمة كشف التسلل (IDS) الفعّالة والذكية للتعامل مع هذه المشكلة، تماشيًا مع التعقيد والتنوع المتزايدين للهجمات الإلكترونية. باستخدام أساليب التعلم العميق (DL)، تهدف الدراسة المُقدَّمة إلى بناء إطار عمل مُتكامل يُعزِّز دقة كشف التسلل وقابلية تعميمه عبر مصادر بيانات مُتعدِّدة. في هذا العمل، استُخدمت ثلاث مجموعات بيانات معروفة، وهي UNSW-NB15 وNSL-KDD وCICIDS2017. يستخدم النهج المُقترَح الشبكات العصبية التلافيفية (CNNs) لاستخراج السمات العميقة من هذه المجموعات. يتم تقليل الأبعاد ويتم التخلص من السمات غير ذات الصلة باستخدام تحليل المُكوِّن الرئيسي (PCA). يُستخرج من خلال دمج مجموعات البيانات الثلاث باستخدام مُرمِّز ذاتي عميق متعدد الوسائط (MDAE) مجموعة بيانات جديدة. يستخدم التصنيف النهائي تقنية الغابة العشوائية (RF)، مما يُصنِّف حركة مرور الشبكة بكفاءة ودقة باستخدام البيانات المُجمَّعة. بفضل الدقة العالية، ودرجة F1، ونسبة التذكر البالغة 99.9%، تُظهر نتائج التقييم دقةً ممتازةً للنموذج تبلغ 99.9%. تُظهر هذه النتائج مدى كفاءة دمج خوارزميات التصنيف الحديثة، واستخراج الميزات، وأساليب دمج البيانات متعددة الوسائط لإنشاء نموذج متطور لكشف التسلل، قادر على الدفاع بكفاءة وثبات ضد الهجمات الصعبة
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