استخدام الشبكات العصبونية في بناء نظام كشف تسلل اعتماداً على مجموعة بيانات قياسية (KDD99)
Abstract
Network security has always been a critical issue when it comes to organizations confidentiality and individuals privacy, especially that delicate and important information is being transferred all the time through networks, also many systems have been increasingly relying on web services, such as e-government services, banking services, e-mail and e-commerce. That's why Intrusion Detection Systems (IDS) have become a very important component, which is widely used to protect information and reduce the damage caused by network attacks and violations.
The main purpose of this research is to build an intrusion detection system using neural networks, and KDDCup 99 data set since it is the mostly used comprehensive data set in intrusion detection domain, and it is shared by many Researchers which provide a great opportunity to compare results. And studying the influence of feature reduction on the process of intrusion detection. First, the preprocessing step was applied on the dataset, then few techniques have been applied on the dataset to decrease the number of the features used in the neural network classifier. The MATLAB software was used to train and test the dataset. The accuracy, detection rate and false rates were measured.
تعد مسألة أمن الشبكات مسألة هامة ودقيقة عندما يتعلق الأمر بخصوصية المنظمات والأفراد, خاصة عند تناقل معلومات مهمة وحساسة عبر هذه الشبكات, من جهة أخرى ازداد اعتماد معظم الأنظمة مؤخرا على خدمات الويب المتطورة سواء كانت خدمات حكومية، أو خدمات مصرفية، أو بريد إلكتروني أو تسويق إلكتروني. كل ما سبق زاد من أهمية أنظمة كشف التسلل التي تعد مكون مهم جدا لحماية المعلومات والحد من الضرر الناتج عن الهجمات والاختراقات الشبكية.
الهدف الرئيسي لهذا البحث هو بناء نظام كشف تسلل شبكي باستخدام الشبكات العصبونية, بالاعتماد على مجموعة البيانات KDDCup99 نظرا لكونها حاليا أشمل مجموعة بيانات مستخدمة في مجال كشف التسلل, كما تمت مشاركتها من قبل العديد من الباحثين مما يتيح فرصة لمقارنة النتائج. بالإضافة إلى دراسة تأثير تخفيض السمات على دقة عملية الكشف. تم بداية معالجة مجموعة البيانات المختارة معالجة تحضيرية, ثم تطبيق عدة تقنيات بهدف تخفيض عدد السمات المستخدمة في مصنف الشبكة العصبونية. تم استخدام برنامج الماتلاب للتدريب ولاختبار مجموعة البيانات وقياس دقة المصنف, بالإضافة إلى قياس معدل الكشف ومعدلات الأخطاء.
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