Category-Constrained Broad Recurrent System for Cloud Anomaly Detection

Anomaly detection has become a key focus in maintaining the stability and reliability of the cloud environment. Although with excellent feature extraction ability, deep learning-based anomaly detection methods entail a time-consuming training process. Broad learning system (BLS) provides an alternative supervised way for efficient training. However, due to the imbalance of the collected cloud computing data in which anomaly accounts for a low proportion, sufficient feature extraction from anomaly behaviors with BLS becomes a challenge. Moreover, the input generation of BLS only considers the independence of data, and the generalization of BLS in the correlation modeling of cloud computing data is limited. To tackle the above issues, we introduce an effective anomaly detector, CatBRS, an improved BLS with rebalance operations. Initially, we employ a hybrid resampling method of SMOTE-Tomek to mitigate data imbalance, retain non-synthetic samples for training, and involve synthetic samples in the input generation later. Subsequently, we extend BLS by refining the process of input generation. This enhanced system employs a simple recurrent architecture to model temporal dynamics. Additionally, it integrates an autoencoder-based model with metric learning to obtain category-constrained discriminant features. The improvement in BLS facilitates more comprehensive feature extraction. Finally, extensive experiments are conducted to evaluate the performance of CatBRS on four benchmark datasets. CatBRS shows improvements of up to 3.81% in AUC and 6.09% in F1 compared to suboptimal baseline methods with a low training cost.