Exploiting spatial correlation to enhance wireless security and facilitate pervasive computing

As more wireless networks are deployed, information provided and shared by wireless systems has become an inseparable part of our social fabric. However, wireless security is often cited as a major technical barrier that must be overcome before widespread adoption of wireless information systems and deployment of pervasive applications can occur. This dissertation is focused on exploiting spatial correlation information to enhance wireless security and facilitate pervasive computing applications.;We first address identity-based attacks, including both spoofing and Sybil, in wireless networks. Because these attacks are especially harmful as the claimed identity of a wireless device is often considered as an important first step in an adversary's attempt to launch a variety of attacks. We propose a generalized attack-detection model that utilizes the spatial correlation of received signal strength (RSS) inherited from wireless devices. We then develop a statistical approach to determine the number of attackers so as to further localize these adversaries. Furthermore, for many people mobile devices are becoming the favored portal to their online social lives. Thus, the identity fraud conducted by malicious mobile agents will have detrimental impact on the successful deployment of mobile pervasive applications. We develop the DEMOTE system, which exploits the correlation within the RSS trace based on each device's identity to detect mobile attackers. Our approaches do not require any changes or cooperation from wireless devices.;Moreover, as the trustworthiness of the location information of wireless devices plays a critical role in the successful development of pervasive location-based applications, we propose an attack-resistant localization technique to mitigate the effects of attacks targeting to localization infrastructures. The proposed attack-resistant approach is not localization algorithm-specific, and is scalable to any localization systems. Additionally, by making use of the existing deployment of wireless infrastructures, we develop a device-free passive intrusion learning system. The proposed system detects the intruders, who do not carry any wireless devices and nor do they cooperate, by capturing the wireless environment changes caused by spatial movement of intruders.;The proposed research work advances the foundation of exploring spatial reasoning for pervasive wireless computing through enhancing wireless network security and developing intrusion learning systems. This work contributes to the successful deployment and adoption of mobile pervasive computing applications.