Mémoire
| Résumé : | This thesis investigates the growing security vulnerabilities in wireless local area networks(WLANs), focusing on the identification and prevention of rogue access points (RAPs)and deauthentication attacks. WLAN adoption is more common in residential and businessenvironments, which raises the risk that malicious parties would exploit weaknessesin Wi-Fi standards. The research employs fingerprinting techniques to detect and isolateundesirable devices in order to improve network security without relying on easily identifiableinformation such as MAC or IP addresses.This research looks at a variety of fingerprinting techniques, including hardware-basedmethods, behavioral techniques, and clock skew analysis. It examines and outlines thebenefits and drawbacks of both passive and aggressive detection methods. The methodologycomprises data collection techniques, analytic procedures, and experimental setupswith an emphasis on signal characteristics and clock-skew analysis for improved detectionaccuracy.The thesis contains a detailed analysis of existing literature, a discussion of WLAN androgue access point design, and a comprehensive evaluation of fingerprinting features atboth the MAC and PHY layers. It also offers a practical illustration of a deauthenticationattack detection system built on Python.By addressing significant research issues regarding the effectiveness of fingerprinting techniquesand real-time detection procedures, this study contributes to the development ofmore robust and reliable wireless network security measures. The findings have significantimplications for network administrators and security specialists trying to protect sensitivedata and maintain network integrity in an increasingly wireless environment. |
