|
Determining methods to secure the process of data fusion against attacks by compromised nodes in wireless sensor
networks (WSNs) and to quantify the uncertainty that may exist in the aggregation results is a critical issue in
mitigating the effects of intrusion attacks. Published research has introduced the concept of the trustworthiness
(reputation) of a single sensor node. Reputation is evaluated using an information-theoretic concept, the Kullback-
Leibler (KL) distance. Reputation is added to the set of security features. In data aggregation, an opinion, a metric
of the degree of belief, is generated to represent the uncertainty in the aggregation result. As aggregate information
is disseminated along routes to the sink node(s), its corresponding opinion is propagated and regulated by Josang's
belief model. By applying subjective logic on the opinion to manage trust propagation, the uncertainty inherent in
aggregation results can be quantified for use in decision making. The concepts of reputation and opinion are
modified to allow their application to a class of dynamic WSNs. Using reputation as a factor in determining interim
aggregate information is equivalent to implementation of a reputation-based security filter at each processing stage
of data fusion, thereby improving the intrusion detection and identification results based on unsupervised
techniques. In particular, the reputation-based version of the probabilistic neural network (PNN) learns the signature
of normal network traffic with the random probability weights normally used in the PNN replaced by the trust-based
quantified reputations of sensor data or subsequent aggregation results generated by the sequential implementation
of a version of Josang's belief model. A two-stage, intrusion detection and identification algorithm is implemented
to overcome the problems of large sensor data loads and resource restrictions in WSNs. Performance of the twostage
algorithm is assessed in simulations of WSN scenarios with multiple sensors at edge nodes for known
intrusion attacks. Simulation results show improved robustness of the two-stage design based on reputation-based
NNs to intrusion anomalies from compromised nodes and external intrusion attacks.
|
