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عنوان البحث
Fast And Accurate Classifying Model For Denial-Of-Service Attacks By Using Machine Learning
عنوان المجلة
The Bulletin Of Electrical Engineering And Informatics (Beei)
ISSN-2302-9285
تفاصيل النشر
سنة النشر - 2022 / الفهرس الاصلي للمجلة - 11 : 3 (عدد الصفحات 8)
تصنيف البحث
تنقيب بيانات وامن الشبكات - المجموعة العلمية
رابط DOI
https://doi.org/10.11591/eei.v11i3.3688
رابط البحث
فتح في موقع المجلة الرسمي
البحث والاستدامة
غير مرتبط باهداف التنمية المستدامة  
البحث والمجتمع
غير محدد
الكلمات المفتاحية
CICIDS2017; Denial of service; Fast DDoS detection; J48 Classifier; Machine learning

اسم الباحثجهة الانتساببلد الباحث
محمد ابراهيم كريم جامعة بابل العراق

A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.