A novel anomaly detection approach based on neural networks

Abstract

In this paper, we introduce a Long Short Term Memory (LSTM) networks based anomaly detection algorithm, which works in an unsupervised framework. We first introduce LSTM based structure for variable length data sequences to obtain fixed length sequences. Then, we propose One Class Support Vector Machines (OC-SVM) algorithm based scoring function for anomaly detection. For training, we propose a gradient based algorithm to find the optimal parameters for both LSTM architecture and the OC-SVM formulation. Since we modify the original OC-SVM formulation, we also provide the convergence results of the modified formulation to the original one. Thus, the algorithm that we proposed is able to process data with variable length sequences. Also, the algorithm provides high performance for time series data. In our experiments, we illustrate significant performance improvements with respect to the conventional methods.