Study on internet of things anomaly data mining method based on improved differential evolution automatic clustering

Due to the diversity of IoT devices and the complexity of the environment, it is difficult to effectively and accurately extract data features, resulting in poor clustering performance, poor accuracy in IoT anomaly data mining, and inability to effectively ensure the security of IoT systems. Therefore, a research on IoT anomaly data mining method based on improved differential evolution automatic clustering is proposed. Through dimensionality reduction of the collected data, the overfitting problem of the mining results is avoided and the mining efficiency is improved. The multi-stage feature selection is expanded to obtain the best feature. Based on this, a differential evolution algorithm is introduced to determine and adjust cluster centres by improving variation factors and cross factors through adaptive strategies, and the K-means automatic clustering algorithm is used to complete abnormal data mining in the network. The results show that the NMI value and ARI value of the proposed method can reach more than 0.95, the AUC value is close to 1, and the mining time is 1.8 s, which has a good clustering effect and can accurately realise the mining of abnormal data.