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Remotely deployed sensor networks are vulnerable to both physical and electronic security breaches. The sensor nodes, once compromised, can send erroneous data to the base station, thereby possibly compromising network effectiveness. The authors assume that sensor nodes are organized in a hierarchy and use offline neural network based learning technique to predict the data sensed at any node given the data reported by its siblings. This allows one to detect malicious nodes even when the siblings are not sensing data from the same distribution. The speed of detection of compromised nodes, however, critically depends on the mechanism used to update the reputation of the sensor nodes over time.
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