Imbalanced SVM‐Based Anomaly Detection Algorithm for?Imbalanced Training Datasets

摘要

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine ( SVM )‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false‐negative rate. This article proposes a new imbalanced SVM (termed Im SVM )‐based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. Im SVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GM ean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate Im SVM . Highly imbalanced training sample sets are constructed. The experimental results show that Im SVM outperforms over‐sampling techniques and several existing imbalanced SVM ‐based techniques.