Comparing Reputation Schemes for Detecting Malicious Nodes in Sensor Networks

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. We assume that sensor nodes are organized in a hierarchy and use an offline neural network-based learning technique to predict the data sensed at any node given the data reported by its siblings in the hierarchy. This allows us 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. We compare and contrast the relative strengths of a statistically grounded scheme and a reinforcement learning-based scheme both for their robustness to noise and responsiveness to change in sensor behavior. We first extend an existing mechanism to improve detection capability for smaller errors. Next we analyze the influence of different discount factors, including unweighted, exponential and linear discounts, on the tradeoff between responsiveness and robustness. We both develop a theoretical analysis to understand the tradeoff and perform experimental verification of our predictions by varying the patterns in sensed data.