Anomaly detection method and apparatus for multi-type data

US11423260B1 · kind B1 · utility

Assignees

• Harbin Institute of Technology (Shenzhen) (Shenzhen Institute of Science and Technology Innovation, Harbin Institute of Technology) · CN
• Dongguan University of Technology · CN

Inventors

• Qing Liao · Lo Wu, CN
• Binxing Fang · 流花街道, CN
• Ye Ding · Cambridge, US

Key dates

• Filing date: Jan 31, 2022
• Grant date: Aug 23, 2022
• Expiry date: Jan 31, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/094
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The present disclosure provides an anomaly detection method and apparatus for multi-type data. According to the anomaly detection method for multi-type data, an adversarial learning network is trained, so that a generator in the adversarial learning network fits a distribution of a normal training sample and learns a potential mode of the normal training sample, to obtain an updated adversarial learning network, an anomaly evaluation function in the updated adversarial learning network is constructed according to a reconstruction error generated during training, and the updated adversarial learning network is constructed into an anomaly detection model, to perform anomaly detection on inputted detection data by the anomaly detection model, to obtain an anomaly detection result. A mode classifier is introduced to effectively resolve difficult anomaly detection when a distribution of detected data is similar to that of normal data, further improving the accuracy of anomaly detection.