An Anomaly Detection Method Based On Deep Learning

Abstract

In order to overcome the difficulty of extracting features from data and improve the accuracy of anomaly detection system, this paper proposes a novel anomaly detection method based on deep learning. We build a deep neural network model with multiple hidden layers to automatically learn features of data before detecting anomaly behaviors. The learned features from this network can enhance the discrimination of different behaviors. Moreover, an exactly sparse auto-encoder (ESAE) is proposed to achieve the pre-training of this network. This method does not require manual extraction of features, and is unsupervised, avoiding the difficulty of providing labeled data. Experimental results show that the proposed method could significantly improve the detection accuracy.

Constituent analysis and quality control of anthocyanin constituents of dried Lycium ruthenicum Murray fruits by HPLC–MS and HPLC–DAD

“…Differences between dried and fresh Lycium ruthenicum Murray fruits did exist in these papers. However, there is no body point to contrast the difference [8][9][10]. In fact, dried Lycium ruthenicum Murray fruits have been mainly sold in market.…”