النظام الذكي المختص بأنظمة مراقبة التسلل الإلكترونية بمناطق خوادم الشبكة

محمد رامي زهير حسن سعيفان; 'Mohamed Rami' Zuher Hassan Isifan

النظام الذكي المختص بأنظمة مراقبة التسلل الإلكترونية بمناطق خوادم الشبكة

dc.contributor.advisor	جاد النجار
dc.contributor.advisor	د. رشيد الجيوسي
dc.contributor.author	محمد رامي زهير حسن سعيفان	ar
dc.contributor.author	'Mohamed Rami' Zuher Hassan Isifan	en
dc.contributor.examiner	د. بديع السرطاوي
dc.contributor.examiner	Dr. Ahmad Alsadeh
dc.date.accessioned	2018-10-07T11:35:21Z
dc.date.available	2018-10-07T11:35:21Z
dc.date.issued	2016-06-04
dc.description.abstract	Prediction of network attacks and machine understandable security vulnerabilities are complex tasks for current available Intrusion Detection System [IDS]. IDS software is important for an enterprise network. It logs security information occurred in the network. In addition, IDSs are useful in recognizing malicious hack attempts, and protecting it without the need for change to client‟s software. Several researches in the field of machine learning have been applied to make these IDSs better a d smarter. In our work, we propose approach for making IDSs more analytical, using semantic technology. We made a useful semantic connection between IDSs and National Vulnerability Databases [NVDs], to make the system semantically analyzed each attack logged, so it can perform prediction about incoming attacks or services that might be in danger. We built our ontology skeleton based on standard network security. Furthermore, we added useful classes and relations that are specific for DMZ network services. In addition, we made an option to mallow the user to update the ontology skeleton automatically according to the network needs. Our work is evaluated and validated using four different methods: we presented a prototype that works over the web. Also, we applied KDDCup99 dataset to the prototype. Furthermore,we modeled our system using queuing model, and simulated it using Anylogic simulator. Validating the system using KDDCup99 benchmark shows good results law false positive attacks prediction. Modeling the system in a queuing model allows us to predict the behavior of the system in a multi-users system for heavy network traffic.	en
dc.identifier.other	21211780
dc.identifier.uri	https://dspace.alquds.edu/handle/20.500.12213/1471
dc.language.iso	en_US
dc.publisher	AL-Quds University	en
dc.publisher	جامعة القدس	ar
dc.subject	علم الحاسوب	ar
dc.subject	Computer Science	en
dc.subject.other	رسالة ماجستير	ar
dc.subject.other	دراسات عليا	ar
dc.subject.other	Higher Studies	en
dc.subject.other	Master Thesis	en
dc.title	النظام الذكي المختص بأنظمة مراقبة التسلل الإلكترونية بمناطق خوادم الشبكة	ar
dc.title	Smart Intrusion Detection System for DMZ	en
dc.type	Thesis

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