Compromised node detection using hierarchical fuzzy logic and feature reduction / Ahmed Shawki Bayoumi Abu Daia ; Supervised Magda B. Fayek , Rabie A. Ramadan

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Ahmed Shawki Bayoumi Abu Daia

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TextLanguage: English Publication details: Cairo : Ahmed Shawki Bayoumi Abu Daia , 2017Description: 133 P. : charts ; 30cmOther title:

إكتشاف النقاط المخترقة في الشبكات اللاسلكية باستخدام المنطق الضبابي الهرمي مع تقليل عدد الخصائص [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: This research proposes a hierarchal fuzzy logic system used for detecting the compromised or attacked nodes in wireless networks. The proposed system is composed of three hierarchal layers and each layer composed of concrete components built using the Fuzzy Unordered Rule Induction Algorithm (FURIA) fuzzy logic. The Particle Swarm Optimization (PSO) technique is used at the data preprocessing phase to reduce the significant features number. We used NSL-KDD dataset for the training and evaluation phases, and the WEKA is the environment used for experiments

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Item type	Current library	Home library	Call number	Copy number	Status	Date due	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.06.M.Sc.2017.Ah.C (Browse shelf(Opens below))		Not for loan		01010110072944000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.06.M.Sc.2017.Ah.C (Browse shelf(Opens below))	72944.CD	Not for loan		01020110072944000

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Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

This research proposes a hierarchal fuzzy logic system used for detecting the compromised or attacked nodes in wireless networks. The proposed system is composed of three hierarchal layers and each layer composed of concrete components built using the Fuzzy Unordered Rule Induction Algorithm (FURIA) fuzzy logic. The Particle Swarm Optimization (PSO) technique is used at the data preprocessing phase to reduce the significant features number. We used NSL-KDD dataset for the training and evaluation phases, and the WEKA is the environment used for experiments

Issued also as CD

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