Analysis of Intrusion Detection System

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dc.contributor.advisorHerendi, Tamás
dc.contributor.authorAl-Aodat, Ashraf Mohammad Jadee
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2020-05-12T10:27:25Z
dc.date.available2020-05-12T10:27:25Z
dc.date.created2020-05-11
dc.description.abstractIntrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this work, we analyze the technologies used in detecting malicious activity in the network. Recently It became common to use machine learning approaches like supervised and unsupervised learning to build such systems. Deep learning has proven to be very successful in many application domains but it has not been investigated well in intrusion detection. This work focuses on deep neural network models such as Deep Convolutional Neural Network, a couple of intrusion detection system. is proposed, implemented and analyzed. The Proposed systems are trained and tested on a modern datasets using GPU. Performance comparisons of proposed models are provided with other classifiers using well-known metrics. The experimental results of the proposed IDS shows promising results for real-world application in anomaly detection systems.hu_HU
dc.description.correctorN.I.
dc.description.courseComputer Sciencehu_HU
dc.description.degreeMSc/MAhu_HU
dc.format.extent35hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/286919
dc.language.isoenhu_HU
dc.subjectIntrusion Detectionhu_HU
dc.subjectDeep Learninghu_HU
dc.subjectMachine Learninghu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titleAnalysis of Intrusion Detection Systemhu_HU
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