Wireless sensor network anomaly detection method

The invention discloses a wireless sensor network anomaly detection method. A fuzzy support vector machine is applied to the field of wireless sensor network anomaly detection based on a drosophila optimization algorithm, and the method comprises the following steps: a training stage: S1, collecting sensor detection data, and preprocessing the data to form a training data set; S2, establishing a wireless sensor network anomaly detection model based on the FSVM technology; S3, establishing an IFOA-FSVM model, and performing anomaly detection training on the data set; in the detection stage: S4,collecting and preprocessing sensor detection data to form a to-be-detected sample; and S5, inputting the to-be-detected sample into an IFOA-FSVM model for detection, and judging whether the to-be-detected sample is abnormal or not. 本发明公开了一种无线传感器网络异常检测方法，基于果蝇优化算法，将模糊支持向量机应用于无线传感器网络异常检测领域包括以下步骤：训练阶段：S1：采集传感器检测数据，并对数据进行预处理，形成训练数据集；S2：基于FSVM技术建立无线传感器网络异常检测模型；S3：建立IFOA-FSVM模型，对数据集进行异常检测训练；检测阶段：S4：采集传感器检测数据，并对数据进行预处理，形成待测样本；S