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العنوان
A Methodology for WLAN Vulnerability Study /
المؤلف
Abdelrahman, Ahmed Ismail Mohamed.
هيئة الاعداد
باحث / Ahmed Ismail Mohamed Abdelrahman
مشرف / Eman Shaaban
مشرف / Heba Khaled
مناقش / Ahmed Mohamed Hamad
تاريخ النشر
2019.
عدد الصفحات
116 P. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
Computer Science (miscellaneous)
تاريخ الإجازة
1/1/2019
مكان الإجازة
جامعة عين شمس - كلية الحاسبات والمعلومات - قسم نظم الحاسبات
الفهرس
Only 14 pages are availabe for public view

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from 116

Abstract

Nowadays, WPA/WPA2 is used for the authentication and encryptsion process of the most used WLANs. PSK mode is the dominant authentication mode for most of WLANs represented in small or non-professional networks. Cracking PSK password is discussed and implemented in many online tools and scientific papers with no clarification of links between the cracking process and WLAN standards. This thesis shows proficiency of all required aspects to cover that link’s gap, and paves the way of proposing security and protection tools. The thesis masters the related IEEE 802.11 MAC layer standards and the used protocols structures that relate to PSK authentication process. WLAN attacks are categorized to locate PSK cracking and define its related attacks and tools. Moreover the different used research platforms in PSK cracking are discussed like GPU, Multi Core CPU, FPGA and Cell BE. To show WLAN Vulnerability, we used the acquired mastered knowledge to design and implement our PSK cracking tool ”Vulnerability Research Study Tool” (VRST). VRST represents a unique edge through illustrating the relations between the cracking steps input and 802.11 standards. To the best of our knowledge, the previously research contributions didn’t reveal the knowhow of extracting the cracking inputs from the raw exchanged data between the Access Point and the client. To accelerate WPA/WPA2 PSK cracking, the single threaded VRST design and implementation is adapted to shared memory parallel platforms: GPU and Multi-Core. Performance results show that the cracking efficiency is upgraded to 16X by utilizing Multi-Core processor and to 41x by using GPU.s