Complicated Imbalanced and Overlapped Data Oversampling Approach via Hypersphere Coverage and Adaptive Differential Evolution for Anomaly Detection of Industrial Internet of Things
Wenli Shang, Lei Ding, Xueying Yang, Zhaojun Gu, He Sui · IEEE Internet of Things Journal · 2025
Anomaly detection is a unique type of classification challenge. The coupling of imbalance, overlap and other complexity of the data such as noise in industrial internet of things (IIoT) scenarios affect the detection accuracy seriously. To address this issue, this article proposes a novel oversampling approach based on synthetic minority oversampling technology via hypersphere coverage and adaptive differential evolution (HCADE-SMOTE). However, overlap intensification caused by generated samples and over-loss of valid information have always been crucial problems for traditional SMOTE-based approaches. In HCADE-SMOTE, we identify error-prone samples including minority noise and boundary samples based on hypersphere coverage algorithm first. Then, we fine-tune the distribution of these error-prone samples before generation with an adaptive differential evolution algorithm. Instead of deletion mechanism, it avoids transitional information loss. With error-prone samples far away from the boundary, HCADE-SMOTE improves the boundary distribution and simplifies the judge the decision boundary for the detection models. Furthermore, minority samples are oversampled based on local hypersphere density and compactness with a weighted SMOTE mechanism to address imbalance problem. The superiority of this HCADE-SMOTE is verified by experiments from optimization of sample distribution, effect of anomaly detection, and statistical tests, compared with 7 well-known SMOTE-based methods. The experimental results show that HCADE-SMOTE is the most prominent to alleviate overlap with Fishers discriminant ratio metric. After HCADE-SMOTE, the detection results reached the best with classification metrics, for the four detection models Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes, and Decision Tree (DT). The statistical tests also prove HCADE-SMOTE has significant difference from other SMOTE-based methods, superior to them.