Wireless sensor networks play the most important role in the internet of things (IoT). Due to the extensive development of sensor networks, the innate limitations and characteristics of the resources in sensors, and heterogeneity of equipment, wireless sensor network has been confronted with security challenges and various vulnerabilities. One way to improve the reliability of the sensor networks is to use abnormal behaviors detection methods in the network. The current paper presents a detection method for abnormal behaviors in a distributed way using a division technique based on entropy and closeness of cumulative observation points. The proposed algorithm for detecting intrusions processed in an intranetwork manner and separates abnormal data from normal data and then classifies it and reports to the higher levels. To investigate the efficiency and accuracy of the detection, multilayer hierarchical topology and simulations based on MATLAB software were employed. The results show high accuracy of the proposed method in detecting abnormal behavior in different levels of wireless sensor networks.

无线传感器网络在物联网（IoT）中扮演着最重要的角色。由于传感器网络的广泛发展，传感器资源的固有局限性和特征以及设备的异质性，无线传感器网络已面临安全挑战和各种漏洞。一种提高传感器网络可靠性的方法是在网络中使用异常行为检测方法。目前的论文提出了一种基于异常和基于累积观测点的接近度的划分技术的异常行为检测方法。所提出的用于检测以内部网络方式处理的入侵并从正常数据中分离出异常数据，然后对其进行分类并报告给更高级别的算法。为了研究检测的效率和准确性，采用了基于MATLAB的多层分层拓扑结构和仿真方法。结果表明，该方法在不同级别的无线传感器网络中检测异常行为的准确性很高。