POSTER: Signal anomaly based attack detection in wireless sensor networks

Authors:
Jeton Bacaj;Leon Reznik
Affiliations:
Rochester Institute of Technology, Rochester, NY, USA;Rochester Institute of Technology, Rochester, NY, USA
Venue:
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Year:
2013

Citing 6
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Abstract

This paper presents a feasibility study of novel attack detection mechanisms in wireless sensor networks (WSN) based on detecting anomalies and changes in sensor signals and data values. Typical WSN attacks are considered in the empirical study of various attack detection techniques utilizing features based on sensor signal strength and other WSN technological parameters and using machine learning classification techniques such as clustering, rule learners, and neural networks. For the attack detection implementation the study employed WSN built from Sun kits available on the market and extended Sensor Network Anomaly Detection System (SNADS) framework of methods and tools.