基于变分自编码器的异常检测算法研究

Research on Anomaly Detection Algorithm Based on Variational Autoencoder

异常检测能够检测出数据中的异常情况,为各类系统正常运转提供重要支撑。提出一种基于变分自编码器的异常检测算法,该算法使用变分自编码器对输入数据进行特征提取,结合深度支持向量网络,压缩特征空间,并寻找最小超球体分离正常数据和异常数据,通过计算数据特征到超球体中心的欧式距离衡量数据的异常分数,并以此进行异常检测。在基准数据集MNIST和Fashion-MNIST上评估该算法,平均AUC分别达0.954和0.935,优于其它优秀算法。实验结果表明,该算法取得较好异常检测效果。

Anomaly detection can detect anomalies in the data and provide important support for the normal operation of various sys⁃tems,which has important practical significance.An anomaly detection algorithm based on variational autoencoder is proposed.The al⁃gorithm uses the variational autoencoder to extract the features of the input data,and then combines the deep support vector network to compress the feature space,and finds the minimum hypersphere to separate the normal data and the abnormal data,calculates the Eu⁃clidean distance from the feature of data to the center of the hypersphere to measure the anomaly score of the data and uses it to detect anomalies.Finally,the algorithm is evaluated on the benchmark datasets MNIST and Fashion-MNIST,the average AUC are 0.954 and 0.935 respectively,which are better than other excellent algorithms.The experimental results show that the proposed algorithm achieves preferable effects.