Obtaining accurate positions of nodes in wireless and sensor networks is important because the location of wireless devices is a critical input to many high-level services. Such services include healthcare monitoring, wildlife animal habitat tracking, emergency rescue and recovery, location-based access control, and location-aware content delivery. However, the localization infrastructure can be subjected to non-cryptographic attacks which cannot be addressed by traditional security services. Further, location services are only useful if the location information is accurate and trustworthy. Thus it is desirable to explore solutions to detect and eliminate these attacks from the network. In this talk, I first describe a generalized localization model and a representative set of localization algorithms. Then, I present our study on the robustness of these algorithms to signal strength attacks.

Next, I present several attack detection schemes for wireless localization systems. We formulate a theoretical foundation for the attack detection problem using statistical significance testing. Our experimental results provide strong evidence of the effectiveness of our approaches with high detection rates and low false positive rates across both an 802.11 (WiFi)) network as well as an 802.15.4 (ZigBee) network in two real office buildings. Finally, we propose a scheme using K-means clustering analysis for both detecting spoofing attacks as well as localizing the positions of the adversaries so that to eliminate the attacks from the network. Additionally, we present GRAIL (General Real-Time Adaptable Indoor Localization) which is a practical implementation of wireless localization. It is designed as a distributed system with multiple software modules that can localize a wide range of wireless devices in challenging environments using customized statistical Bayesian Networks.

Yingying (Jennifer) Chen is a Ph.D candidate and an instructor in the Computer Science Department at Rutgers University. Her research interests span network and information system security, software engineering and security in distributed systems, network systems, as well as wireless and sensor networks. She is interested in using statistical methods and machine learning techniques to classify and model system and security problems. She is a member of Wireless Information Network Laboratory (WINLAB) at Rutgers University. She has won the Best Technological Innovation Award for the Real Time Statistical Bayesian Positioning system at the 3rd International TinyOS Technology Exchange in 2006. Concurrent to her studies, Yingying is employed at Bell Laboratories (Lucent Technologies) as a software architect and project lead. She is involved in designing and developing numerous projects at Bell Labs, ranging from end-to-end network management systems to integrating voice/data services with ATM switches.