Document Details

Document Type : Thesis 
Document Title :
PERFORMANCE ANALYSIS OF FEATURE SUBSET SELECTION TECHNIQUES FOR INTRUSION DETECTION
تحليل أداء تقنيات اختيار مجموعة الميزات لاكتشاف التسلل
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : An intrusion detection system (IDS) is one of the main defense lines that provide security to data, information, and computer networks. The problems of this security system are increased processing time, high false alarm rate, and low detection rate that are caused by a large amount of data that contain various irrelevant and redundant features. Feature selection (FS) can solve these problems by reducing the number of features. Choosing appropriate feature selection methods that can reduce the number of features without a negative effect on classification accuracy is a major challenge. This challenge motivated us to investigate the application of different FS techniques in intrusion detection. The performances of the selected techniques such as genetic algorithm, greedy search, and back elimination are analyzed, addressed, and compared with existing techniques. Several machine learning methods such as support vector machine (SVM) and multilayer perceptron (MLP) are used as objective function for selected FS techniques as well as classification process. The CIC-IDS-2017, CSE-CIC-IDS-218, and NSL-KDD datasets are considered for the experiments. The efficiency of the proposed models was proved in the experimental results, which indicated that it had highest accuracy in the selected detests 
Supervisor : Dr. Iftikhar Khan 
Thesis Type : Master Thesis 
Publishing Year : 1444 AH
2023 AD 
Added Date : Saturday, June 17, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
يوسف هذيل المغذويAl-Maghwi, Youssef HudhailResearcherMaster 

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 49602.pdf pdf 

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