Automatic Test Data Generation for Big Data Input Values
| المؤلف الرئيسي: | Qtaish, Osama (مؤلف) |
|---|---|
| مؤلفين آخرين: | H.Eljinini, Mohammad Ali (Advisor) , Qtaish, Osama (Advisor) |
| التاريخ الميلادي: |
2020
|
| موقع: | عمان |
| الصفحات: | 1 - 51 |
| رقم MD: | 1103128 |
| نوع المحتوى: | رسائل جامعية |
| اللغة: | الإنجليزية |
| الدرجة العلمية: | رسالة ماجستير |
| الجامعة: | جامعة الاسراء الخاصة |
| الكلية: | كلية تكنولوجيا المعلومات |
| الدولة: | الاردن |
| قواعد المعلومات: | Dissertations |
| مواضيع: | |
| رابط المحتوى: |
عدد مرات التحميل
17
| المستخلص: |
There is needed for the efficient intrusion detection system working over the network system to detect the whole possible attacks. Intrusion detection is so much popular since the last two decades, where intruders attempted to break into or misuse the system. There are many techniques used in intrusion detection (IDS) for protecting computers and networks from network-based and host-based attacks. In this thesis, the proposed approach presents a new model for IDS using a bat algorithm that aims to select the best features using big data. The proposed approach divided into several phases to extract and find all possible features that effect directly in the detection process. The proposed approach was tested using the KNIME Analytics Platform based on Support Vector Machine (SVM) and Naïve base classifiers. The experiment results give a high accuracy (97.52%) with reducing the error classification into (2.47%) using the SVM classifier. |
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