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|a eng
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| 100 |
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|9 593788
|a Qtaish, Osama
|e مؤلف
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| 245 |
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|a Automatic Test Data Generation for Big Data Input Values
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| 260 |
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|a عمان
|c 2020
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| 300 |
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|a 1 - 51
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| 336 |
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|a رسائل جامعية
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| 502 |
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|b رسالة ماجستير
|c جامعة الاسراء الخاصة
|f كلية تكنولوجيا المعلومات
|g الاردن
|o 0025
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| 520 |
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|a 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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| 653 |
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|a التنقيب عن البيانات
|a الاختبار التلقائي
|a البيانات الضخمة
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| 700 |
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|9 593786
|a H.Eljinini, Mohammad Ali
|e Advisor
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| 700 |
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|9 593788
|a Qtaish, Osama
|e Advisor
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| 856 |
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|u 9802-021-007-0025-T.pdf
|y صفحة العنوان
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| 856 |
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|u 9802-021-007-0025-A.pdf
|y المستخلص
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| 856 |
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|u 9802-021-007-0025-C.pdf
|y قائمة المحتويات
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| 856 |
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|u 9802-021-007-0025-F.pdf
|y 24 صفحة الأولى
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| 856 |
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|u 9802-021-007-0025-1.pdf
|y 1 الفصل
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| 856 |
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|u 9802-021-007-0025-2.pdf
|y 2 الفصل
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| 856 |
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|u 9802-021-007-0025-3.pdf
|y 3 الفصل
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| 856 |
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|u 9802-021-007-0025-4.pdf
|y 4 الفصل
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| 856 |
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|u 9802-021-007-0025-5.pdf
|y 5 الفصل
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| 856 |
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|u 9802-021-007-0025-R.pdf
|y المصادر والمراجع
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| 856 |
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|u 9802-021-007-0025-S.pdf
|y الملاحق
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| 930 |
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|d y
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| 995 |
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|a Dissertations
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| 999 |
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|c 1103128
|d 1103128
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