نهج متكامل للكشف عن التصيد الاحتيالي باستخدام K-Means والخوارزمية الجينية
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الكلمات المفتاحية

AdaBoost; ensemble learning; feature selection; genetic algorithm; K-means clustering; machine learning; phishing detection

كيفية الاقتباس

نهج متكامل للكشف عن التصيد الاحتيالي باستخدام K-Means والخوارزمية الجينية. (2025). مجلة الخوارزمي الهندسية, 21(2), 117-135. https://doi.org/10.22153/kej.2025.04.011

الملخص

المستخلص

 

الكشف عن التصيد الاحتيالي هو مشكلة حرجة في مجال الأمن السيبراني، وأكبر تحدٍ هو كيفية استخدام التعلم الآلي مع طريقة فعالة لاختيار الميزات لتحديد المواقع الضارة بدقة. يقدم هذا البحث نظامًا للكشف عن التصيد الاحتيالي يتكون من مرحلتين رئيسيتين، يتم فيهما استخدام اختيار الميزات غير المراقب والتصنيف المراقب.في المرحلة الأولى، تُستخدم خوارزمية التحسين الجيني  (GA)لتحديد أفضل مجموعة من الميزات التي يتم استخدامها بواسطة خوارزمية التجميع K-means لتقسيم مجموعة البيانات إلى مجموعات تحمل سمات متشابهة.أما في المرحلة الثانية، فيتم استخدام خوارزمية التحسين الجيني (GA) مرة أخرى لتحديد أفضل مجموعة ميزات داخل كل مجموعة، مما يعزز عملية التصنيف. في النهاية، يتم تطبيق تقنية التجميع بالتصويت Voting Ensemble)، حيث يتم دمج نماذج Support Vector Machine (SVM) وRandom Forest (RF) وXGBoost و(AdaBoost باستخدام آلية تصويت ناعمة لتجميع التنبؤات.تم استخدام مجموعة بيانات خاصة بالكشف عن التصيد الاحتيالي لصفحات الويب في هذا البحث، تحتوي على 11,430 عنوان URL و87 ميزة.أظهرت النتائج أن تقنية التجميع بالتصويت تحقق دقة تصل إلى 99% عند استخدام اختيار الميزات، مقارنة بـ 77.3% دون استخدام اختيار الميزات. يُظهر اختيار الميزات المُحسَّن باستخدام خوارزمية (GA) تحسينًا كبيرًا في أداء النموذج، من خلال تقليل التعقيد الحسابي وتحسين المؤشرات الرئيسية مثل الدقة (Accuracy)، ومعامل التحديد (Precision)، ودرجة F1 (F1-score). إضافة إلى ذلك، تُظهر النتائج عبر أربع مجموعات البيانات أن خوارزمية K-means تُسهم بشكل إيجابي في تحسين دقة التصنيف ضمن مجموعات بيانات معينة. تُثبت النتائج المحققة أن دمج اختيار الميزات مع تقنيات التعلم المجمع يعد حلاً فعالاً للكشف عن التصيد الاحتيالي، ويظهر قابلية التطبيق والكفاءة لهذا الحل في الاستخدامات الواقعية.

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