Zhejiang Gongshang University

Jian Fang

Xiaoning Jiang

Yuhan Zhou

Hongmin Xie

Kai Liu

Zhendong Yu

Kelei Jiang

Fengqing Zhang

Jia'nan Qi

Jianan Qi

Hanqi Liu

Jiahao Yao

Zhenye Xu

A system and method for authentication code generation based on adversarial machine learning are provided in this disclosure. The method includes a defensive authentication code generation system, an authentication code formation module, an authentication code scheduling module, an authentication-code adversarial processing center, an attack sample generation module, a verification and error reporting system, a division module, a grouping and distribution system, a category checking module, a data recording unit, a detection module and an integration terminal. In the system and method for authentication code generation based on adversarial machine learning according to the disclosure, attack scenes are simulated for continuous training for the authentication code, error-reporting data are recorded and optimized into the defensive authentication code generation system, so as to improve defense performance of the authentication code.

System and method for authentication code generation based on adversarial machine learning

The disclosure provides a method, a device and a system for fault detection. The fault detection method includes following steps: acquiring interactive data generated during a working process of a target Internet of Things system, and generating data to be detected based on the interactive data; detecting whether the data to be detected is fault data or not, and performing an emotion analysis on the data to be detected which is judged as the fault data to obtain a corresponding emotion label; and extracting a corresponding fault type from a preset fault emotion mapping table based on the emotion label and outputting the fault type. In this disclosure, the interactive data is analogized to human emotions, and the faults are accurately found through the emotional analysis on the data, thereby achieving the fault detection of the Internet of Things system in a convenient, flexible and accurate manner.

Method, device and system for fault detection

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Hangzhou, China

Jian Fang