【摘要】 大规模高维不平衡数据是异常检测中的重大挑战。单类支持向量机在处理不平衡数据方面非常有效，但不适合大规模高维数据，同时单类支持向量机的核函数对检测性能也具有重要的影响。文中提出了一个深度自编码器与单类支持向量机相结合的异常检测模型，深度自编码器不仅负责提取特征和降维，同时拟合出了一个自适应核函数。深度自编码器与单类支持向量机共享损失函数，实现了端到端的训练。作为一个整体，模型采用梯度下降法进行联合训练。在4个公开数据集上与其他异常检测方法进行了对比实验。实验结果表明，在AUC以及召回率(RECALL)方面，所提模型的性能优于单核和多核单类支持向量机以及其他模型，并且所提模型在不同异常率时是鲁棒的，在时间复杂度方面也具有非常大的优势。更多还原

【Abstract】 Large-scale high-dimensional unbalanced data handling is a major challenge in anomaly detection.One-class support vector machine(OCSVM) is very efficient at handling unbalanced data, but it is not suitable for large-scale high-dimensional dataset.Meanwhile, the kernel function of OCSVM also has an important influence on the detection performance.An anomaly detection model combining a deep auto-encoder and a one-class support vector machine is proposed.The deep auto-encoder is not only responsible for extracting features and dimensionality reduction, but also mapping an adaptive kernel function.As a whole, the model adopts the gradient descent method to carry out joint training and realizes end-to-end training.Experiment is conducted on four public datasets and compared with other anomaly detection methods.Experimental results show that the proposed model has better performance than single-kernel or multi-kernel one-class support vector machines and other models in terms of AUC and RECALL,and the proposed model is robust at different anomaly rate and has great advantages in time complexity.更多还原