Abstract: 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.

Abstract: A method and a system for filtering ill corpus are provided. The method includes following steps: acquiring a text corpus to be recognized, and preprocessing the text corpus to be recognized to obtain a basic text corpus; extracting entities in the basic text corpus, and performing matching search on the entities of the basic text corpus according to an ill-text knowledge graph to obtain a first recognition result; detecting and recognizing the basic text corpus according to a corpus recognition model to obtain a second recognition result; and filtering the text corpus to be recognized according to the first or/and the second recognition result, and updating the ill-text knowledge graph according to the second recognition result. With semantic network essence and strong correlation ability of knowledge graph technology, candidate ill entities can be obtained, thus facilitating filtering of obscure ill information in forms of pinyin, homophonic words and split words.

Abstract: 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.