公开(公告)号：US20230362180A1

公开(公告)日：2023-11-09

申请号：US17739968

申请日：2022-05-09

Applicant: Oracle International Corporation

Inventor： Milos Vasic ,  Saeid Allahdadian ,  Matteo Casserini ,  Felix Schmidt ,  Andrew Brownsword

IPC: H04L9/40 ,  G06N20/20

CPC classification number: H04L63/1425 ,  G06N20/20

Abstract: Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.