Document Details

Document Type : Thesis 
Document Title :
A Hybrid Intrusion Detection Systems Approach for IEEE 802.11 Wireless Networks
نظام كشف التسلل الهجين على الشبكات اللاسلكية IEEE 802.11
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The IEEE 802.11i protocol is the current security standard for WLANs. While it has strong security mechanisms such as Advanced Encryption Standard for encrypting and the four-way handshake protocol for authentication, it is still vulnerable to a number of serious attacks such as deauthentication and disassociation flooding. Various intrusion detection techniques are proposed by the research community to detect known and zero-day WLAN attacks. Nevertheless, further efforts are needed to improve the detection performance using a benchmark 802.11 dataset that contains both normal traffic and intrusive traffic of all known attacks. The present research starts by investigating all serious attacks and vulnerabilities in IEEE 802.11i networks. Next we provide a comprehensive survey of the proposed intrusion detection systems in the literature to find out their merits and limitations. This is followed by designing and implementing a prototype for a hybrid real-time network based WLAN intrusion detection system that employs signature and anomaly detection methods. Using signature detection can improve the performance of the developed intrusion detection system by increasing the true positive rate while anomaly detection can detect zero-day attacks. In addition to the signature rules, we considered both C4.5 classifier and Averaged One-Dependence Estimator (AODE) for anomaly detection. The developed system is evaluated in terms of precision and recall, providing three contributions. Firstly a novel technique is developed for effective feature selection based on filtering model and knowledge of WLAN attack footprints. Secondly, it improves classification accuracy, compared with recently published results, and dramatically increases the classification speed by minimizing the training time and the classification attributes. Thirdly, it offers a high performance real time hybrid WLAN intrusion detection system. The prototype is implemented and tested on 1.7 GHz i5 PC with 12 GB RAM. The experimental results show that the implemented system has a fast learning time of 45 seconds and a high classification performance of 99.6% precision, 98.11% recall, and an overall accuracy of 99.82%. 
Supervisor : Prof. Dr. Mohamed Ashraf Madkour 
Thesis Type : Master Thesis 
Publishing Year : 1438 AH
2017 AD 
Added Date : Thursday, June 1, 2017 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
إسحاق سيد عبداللهabdullah, Ishaqu SayedResearcherMaster 

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