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 attack detection implementation the study employed WSN built from Sun kits available on the market and extended Sensor Network Anomaly Detection System (SNADS) framework of methods and tools.