Comparing reputation schemes for detecting malicious nodes in sensor networks

  • Mukherjee P
  • Sen S
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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. 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.

Author-supplied keywords

  • Q-learning
  • beta-reputation
  • detection
  • reputation management
  • security
  • sensor networks

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  • Partha Mukherjee

  • Sandip Sen

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