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Class distribution asymmetry (imbalanced data) is a prevalent problem in the field of Industrial Internet cybersecurity, where normal data far outnumber abnormal data. This causes traditional machine learning classifiers to be biased towards the majority class, severely degrading their attack detection capability. To address this issue while meeting the requirement for traceability of the decision-making process in industrial scenarios, this paper proposes an imbalanced data classification method based on the Belief Rule Base (BRB). First, the Cluster-Based Oversampling (CBO) algorithm is employed to restore the symmetry of class distribution at the data level. Then, the Evidential Reasoning (ER) iterative algorithm is used to perform attribute fusion, which reduces the number of antecedent attributes of BRB while maintaining the information, effectively alleviating the rule explosion problem. Finally, interpretable classification is realized based on BRB, and the Circle chaotic mapping Gray Wolf Optimizer (Circle-GWO) algorithm is introduced to complete model construction, parameter optimization and fine-tuning. Experimental results on the UNSW-NB15 and TONIoT datasets demonstrate that the proposed method can effectively handle imbalanced data classification tasks in this field, providing a practical technical solution to improve the accuracy and efficiency of cybersecurity decision-making in the Industrial Internet.
Zhao et al. (Wed,) studied this question.