Cross-layer traffic analysis countermeasures against adaptive attackers of wireless sensor networks

Abstract

In most Wireless Sensor Network (WSN) applications the sensor nodes forward their measurements to a central base station (BS). The unique role of the BS makes it a natural target for an adversary’s attack. Even if a WSN employs conventional security mechanisms such as encryption and authentication, an adversary may apply traffic analysis techniques to locate the BS. This motivates a significant need for improved BS anonymity to protect the identity, role, and location of the BS. Previous work presented distributed beamforming as a very effective anonymity-boosting technique. However, such work assumed that the adversary is unaware of the countermeasure, and thus the anonymity performance could be unattainable. In this paper we extend our preliminary work from Ward and Younis (Proceedings of the IEEE military communications conference (MILCOM 2016), Baltimore, MD, 2016) to show that the adversary could adapt the attack strategy when knowing of the use of distributed beamforming. We analyze two strategies for such an adaptive attack, one using Evidence Theory and one using Traffic Volume. We then develop a cross-layer countermeasure that incorporates distributed beamforming to successfully misdirect such an adaptive adversary and boost BS anonymity. The effectiveness of our approach is validated through simulation.