كشف التسلل بكفاءة من خلال دمج خوارزميات الذكاء الاصطناعي وطرق اختيار الميزات
محتوى المقالة الرئيسي
الملخص
أبرزت حالات الاقتحام الأخيرة الحاجة إلى أنظمة الكشف عن اقتحام الشبكة في مقاومة هجمات الشبكة الأكثر تعقيدًا، مع نمو الاتصال بالإنترنت وحجم حركة المرور. كثيرا ما تستخدم أنظمة الكشف عن التسلل تقنيات مثل التعرف على الأنماط واستخراج البيانات لتحديد أنشطة الشبكة كالمعتاد أو الهجوم. نظام الكشف عن التسلل (IDS) هو تقنية نشطة للكشف عن التسلل تقوم تلقائيًا باكتشاف وتصنيف عمليات الاقتحام والاعتداء وانتهاكات السياسة الأمنية على مستوى الشبكة والمضيف. يشير بحثنا إلى أن طرق التعلم الآلي مثل Naiseve Bayes (NB) و K-Nearest Neighbor (KNN) و Decision Tree (DT) قد تعزز فعالية نظام الكشف عن التطفل. دقة الكشف والدقة F1-score والاستدعاء ووقت التنفيذ هي مؤشرات أداء تستخدم لقياس الفعالية. لزيادة كفاءة الكشف، والدقة، وتقليل وقت التنفيذ، تم تطبيق مناهج اختيار الميزات بما في ذلك ANOVA و Mutual Information (MI) و chi-squared (CH-2)، وكانت كل هذه الاستراتيجيات فعالة. عند استخدام ANOVA بنسبة 10٪ من الميزات، تحصل جميع المصنفين على أكبر نتيجة، بدقة 99.99٪ و DT في وقت 0.0089ms
تفاصيل المقالة
كيفية الاقتباس
تواريخ المنشور
الإستلام
الموافقة
النشر الالكتروني
المراجع
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