Research On Security Mechanisms For Wireless Networks Using Physical Layer Information

Wireless network has become a predominant means of communication.However,the prob-lem of unauthorized access to Wi-Fi networks has become increasingly severe.Other security issues such as wireless network intrusions,pose serious threats to normal users of Wi-Fi net-works.Although using the latest encryption techniques are aiming to solve the problem of net-work intrusion,there still exist password leakage and password cracking problems in practice,making password based authorization invalid.In this research,we focus on the wireless security problem by taking advantage of the unique characteristics of physical layer information,such as Received Signal Strength(RSS)and Channel State Information(CSI).We seek to restrict the Wi-Fi coverage to the users only within certain physical area.The key innovation of this paper is that:(1)Location Aware Wi-Fi Access Control.We develop and implement a Crowdsourced Location Aware Wi-Fi Access Control scheme using physical layer information.It crowdsources both CSI and RSS values of already validated users to classify future users.We propose and use two CSI metrics in this paper:CSI Cross-Antenna Stability Metric and CSI Cross-Frame Stability Metric,which summarize well the spatial and temporal CSI characteristics respectively.We also design a practical scheme to detect directional attack.Evaluation results show that the proposed scheme performs well in both environments,allowing most valid users inside the area to access the network,while the chance that invalid users outside the boundary may access the network is small.(2)Mobile Devices Grouping.The ability to detect which wireless mobile devices are used by the same person from Wi-Fi Access Point(AP)could enable many potential applications,including device authentication and user-oriented devices isolation,which will be beneficial to network management.This paper brings this feature to commercial off-the-shelf(COTS)Wi-Fi AP,by proposing an on-body mobile devices grouping scheme.Firstly,the proposed scheme extracts the trajectory and gait characteristics from wireless signals when the user is walking.Secondly,it utilizes a semi-supervised clustering algorithm to measure the similarity of wireless signals between devices.Finally,if the devices are grouped into the same cluster,they are con-sidered to be carried by the same person.The proposed scheme is software-based and designed to be straightforward for integration with commercial APs.(3)Cross-Technology Coexistence and Spoofing Attack Detection.In this paper,we pro-pose a scheme to enable the cross-technology coexistence and spoofing attack detection for het-erogeneous wireless networks by using physical layer information.The most challenging task in this work is how to detect the packet and measure the physical layer information between cross-technology devices,especially from ZigBee to Wi-Fi.To overcome this challenge,we propose a model to detect ZigBee packets and measure the corresponding received signal strength on Wi-Fi devices.Then,we design a collaborative mechanism between Wi-Fi and ZigBee devices to detect the spoofing attack.