Proceedings of SPIE Defense, Security & Sensing, vol. 7709, Orlando, Florida (2010)
Because sensor networks are often deployed in hostile environments where their security and integrity may be compromised, it is essential to maximize the reliability and trustworthiness of existing and envisioned sensor networks. During operations, the sensor network must be robust to deception, node compromise, and various other attacks, while maintaining the operator’s situational awareness regarding the health and integrity of the system. To address these needs, we have designed a Framework to Ensure and Assess Trustworthiness in Sensor systems (FEATS) to identify attacks on sensor system integrity and inform the operator of sensor data trustworthiness. We have developed and validated unsupervised anomaly detection algorithms for sensor data captured from an experimental acoustic sensor platform under a number of attack scenarios. The platform, which contains four audio microphones, was exposed to two physical attacks (audio filtering and audio playback) as well as a live replay attack (replaying live audio data that is captured at a remote location), which is analogous to a wormhole attack in the routing layer. With our unsupervised learning algorithms, we were able to successfully identify the presence of various attacks.
1 Charles River Analytics
2 Massachusetts Institute of Technology
3 Air Force Research Laboratory, Sensors Directorate
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