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This paper presents a feasibility study of novel attack detection mechanisms in wireless sensor networks (WSN) based on detecting anomalies and changes in sensor signals and data values. Typical WSN attacks are considered in the empirical study of various attack detection techniques utilizing features based on sensor signal strength and other WSN technological parameters and using machine learning classification techniques such as clustering, rule learners, and neural networks. For the attackdoi:10.1145/2508859.2512508 dblp:conf/ccs/BacajR13 fatcat:3zygzi4adfgofaexpifjefoq4u