Malicious node detection in wireless sensor networks using weighted trust evaluation
Proceedings of the 2008 Spring simulation multiconference
A distributed adaptive scheme for detecting faults in wireless sensor networks
WSEAS TRANSACTIONS on COMMUNICATIONS
Redundancy and its applications in wireless sensor networks: a survey
WSEAS Transactions on Computers
A view upon redundancy in wireless sensor networks
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
Self-destruction procedure for mesh wireless sensor networks
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Weighted trust evaluation-based malicious node detection for wireless sensor networks
International Journal of Information and Computer Security
Distributed data-theft detection in wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
DBOD-DS: distance based outlier detection for data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Anomaly detection in wireless sensor networks: A survey
Journal of Network and Computer Applications
An adaptive outlier detection technique for data streams
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Online outlier detection for data streams
Proceedings of the 15th Symposium on International Database Engineering & Applications
ACMOS'09 Proceedings of the 11th WSEAS international conference on Automatic control, modelling and simulation
Efficient estimation of dynamic density functions with an application to outlier detection
Proceedings of the 21st ACM international conference on Information and knowledge management
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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In this paper we propose a strategy based on past/present values provided by each sensor of a network for detecting their malicious activity. Basically, we will compare at each moment the sensor's output with its estimated value computed by an autoregressive predictor. In case the difference between the two values is higher then a chosen threshold, the sensor node becomes suspicious and a decision block is activated.