With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks. With the increasing deployment of wireless sensordevices and networks, security becomes a critical challenge for sensor networks. In this paper, a scheme using data mining is proposed for routing anomalydetection in wireless sensor networks. The schemeuses the the Apriori algorithm to extract traffic patterns from both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model. Through thecombination of these two algorithms, routing attacks be detected effectively and automatically. Themain advantage of the proposed approach is that it is able to detect new attacks that have not previouslybeen seen.Moreover , the proposed detection scheme based on no priori knowledge and then can be applied to a wide range of different sensor networks for a variety of routing attacks.