Secure Sensor Network Aggregation and Detection of Replicated Nodes

Abstract

The low-cost, off-the-shelf hardware components in unshielded sensor-network nodes leave them vulnerable to compromise. With little effort, an adversary may capture nodes, analyze and replicate them, and surreptitiously insert these replicas at strategic locations within the network. We propose two algorithms, Randomized Multicast and Line-Selected Multicast, based on emergent properties to defend against these attacks.

Malicious nodes can also prevent data aggregation algorithms from obtaining the correct aggregation result. We present the first algorithm for provably secure hierarchical in-network data aggregation. Our algorithm is guaranteed to detect any manipulation of the aggregate by the adversary beyond what is achievable through direct injection of data values at compromised nodes.

These algorithms were developed in collaboration with Haowen Chan, Virgil Gligor, Bryan Parno, and Dawn Song.

Bio

Adrian Perrig is an Assistant Professor in Electrical and Computer Engineering, Engineering and Public Policy, and Computer Science at Carnegie Mellon University. He earned his Ph.D. degree in Computer Science from Carnegie Mellon University, and spent three years during his Ph.D. degree at University of California at Berkeley. He received his B.Sc. degree in Computer Engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL). Adrian's research interests revolve around building secure systems and include Internet security, security for sensor networks and mobile applications. More information about his research is available on Adrian's web page. Adrian is a recipient of the NSF CAREER award in 2004, the IBM faculty fellowship in 2004 and 2005, and the Sloan research fellowship in 2006.