Malicious traffic analysis in wireless sensor networks using advanced signal processing techniques

Abstract

The recent advances in micro-sensor hardware technologies, along with the invention of energy-efficient protocols, have enabled a world-wide spread in wireless sensor networks deployment. These networks are used for a large number of purposes, while having small maintenance and deployment costs. However, as these are usually unattended networks, several security threats have emerged. In this work, we show how an adversary can overhear the encrypted wireless transmissions, and detect the periodic components of the wireless traffic that can further reveal the application used in the sensor network. Traffic analysis is performed in a very energy-efficient way using the compressed sensing principles. Furthermore, the periodic components are detected using the Lomb-Scargle periodogram technique.