TY - GEN
T1 - Using learned data patterns to detect malicious nodes in sensor networks
AU - Mukherjee, Partha
AU - Sen, Sandip
PY - 2008
Y1 - 2008
N2 - As sensor network applications often involve remote, distributed monitoring of inaccessible and hostile locations, they 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 consider sensor nodes organized in a hierarchy where the non-leaf nodes serve as the aggregators of the data values sensed at the leaf level and the Base Station corresponds to the root node of the hierarchy. To detect compromised nodes, we use neural network based learning techniques where the nets are used to predict the sensed data at any node given the data reported by its neighbors in the hierarchy. The differences between the predicted and the reported values is used to update the reputation of any given node. We compare a Q-learning schemes with the Beta reputation management approach for their responsiveness to compromised nodes. We evaluate the robustness of our detection schemes by varying the members of compromised nodes, patterns in sensed data, etc.
AB - As sensor network applications often involve remote, distributed monitoring of inaccessible and hostile locations, they 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 consider sensor nodes organized in a hierarchy where the non-leaf nodes serve as the aggregators of the data values sensed at the leaf level and the Base Station corresponds to the root node of the hierarchy. To detect compromised nodes, we use neural network based learning techniques where the nets are used to predict the sensed data at any node given the data reported by its neighbors in the hierarchy. The differences between the predicted and the reported values is used to update the reputation of any given node. We compare a Q-learning schemes with the Beta reputation management approach for their responsiveness to compromised nodes. We evaluate the robustness of our detection schemes by varying the members of compromised nodes, patterns in sensed data, etc.
UR - http://www.scopus.com/inward/record.url?scp=39149083439&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-77444-0_35
DO - 10.1007/978-3-540-77444-0_35
M3 - Conference contribution
AN - SCOPUS:39149083439
SN - 3540774432
SN - 9783540774433
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 339
EP - 344
BT - Distributed Computing and Networking - 9th International Conference, ICDCN 2008, Proceedings
PB - Springer Verlag
T2 - 9th International Conference on Distributed Computing and Networking, ICDCN 2008
Y2 - 5 January 2008 through 8 January 2008
ER -