تقييم مقارن لأنظمة اكتشاف الاختراق المعتمدة على التعلم الآلي في البيئات السحابية
محتوى المقال
الملخص
ظهرت الحوسبة السحابية مع التقدم المتزايد في تكنولوجيا الاتصالات وتبادل البيانات. حيث تعتمد اليوم، العديد من الشركات والمؤسسات على حلول الحوسبة السحابية وتشارك خدماتها في السحابة للاستفادة مما توفره من مرونة وقابلية للتوسع. ولكن، على الرغم من المزايا العديدة، فإن الحوسبة السحابية أصبحت بشكل متزايد عرضة لمجموعة متنوعة من التهديدات الأمنية التي يمكن أن تعطل الخدمات والأعمال. ولذلك، فإن هناك حاجة ماسة إلى تعزيز أمن بيئات الحوسبة السحابية. في هذا السياق، يعد توفير حلول حماية قوية، مثل أنظمة اكتشاف الاختراق، أمرًا ضروريًا للتخفيف من التهديدات المحتملة وحماية البيانات الحساسة. ومع ذلك، لمواجهة التطور المستمر للتهديدات السيبرانية، يجب أن تمتلك أنظمة كشف الاختراق هذه القدرة على التطور والتكيف. وفي هذا الصدد، فإن يعد دمج تقنيات التعلم الآلي أمرًا ضروريًا لاكتشاف مجموعة واسعة ومتنوعة من التهديدات السيبرانية، وبالتالي تعزيز حماية وأمن البيئة.
يركز البحث على هذا التكامل بين أنظمة اكتشاف الاختراق وتقنيات التعلم الآلي. بالإضافة إلى تطبيق طرق اختيار الميزات لتحديد المؤشرات الرئيسية والأكثر أهمية لاكتشاف الهجمات. حيث تقدم الدراسة تحليلاً مقارنًا لخمس تقنيات تعلم آلي، باستخدام طريقتين مختلفتين لاختيار الميزات لتقييم كفاءة هذه التقنيات في تعزيز حماية وأمن البيئات السحابية. باستخدام مجموعة بيانات حديثة ومعتبرة في هذا المجال وبالتركيز على أنواع الهجمات التي تشكل التهديد الأكبر للبيئات السحابية، تقدم نتائجنا التجريبية تقييماً شاملاً لهذه التقنيات، بالاعتماد على مجموعة متنوعة من مقاييس الأداء لخوارزميات التعلم الآلي.
الكلمات المفتاحية
Intrusion Detection Systems، Machine-Learning، Cloud Environments، Feature Selection، cybercrimes
التنزيلات
المراجع
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