Tongji University

Other

Chungang Yan

Zhijun Ding

Changjun Jiang

Hongzhong Chen

Hongbo Zhang

Bing Xu

Pengwei Wang

Jian Yu

Mingjie Zhong

Wangyang Yu

Haichun Sun

Zhaohui Zhang

Junzhu Zhong

Yaying Zhang

Youqun Shi

A cross-domain cabin computing system and a cross-domain cabin computing method based on data resource distribution are provided, including: a cabin generation and management system obtains a data resource distribution map from a virtual data center system according to a cabin data and resource request, obtains a cross-domain resource directory from a cross-domain resource management system, and calculates a list of resource requirements of cabins; the cross-domain resource management system sends a resource request to a public cloud or a non-cloud resource to which resources belong based on the list of resource requirement of the cabins, and send information signaling that the resource request is successful to the cabin generation and management system to enable the cabin users to establish cabins based on cross-domain resources and data resources corresponding to the data resource distribution map, thereby realizing cabin computing.

Cross-domain cabin computing system and method based on data resource distribution

The present invention discloses a method and system configured for monitoring and verifying software behavior, comprising: receiving, by a software behavior verification system based on a physical hardware system, legal user behavior data containing user activities performed during legal electronic transactions and storing the legal user behavior data as a software behavior model; monitoring, by a software behavior monitor, data packets transmitted in a transaction, and sending data packets to the software behavior verification system; retrieving, by the behavior verification system, expected key sequences and information in the data packets; comparing the key sequences and information retrieved from the data packets with that of the software behavior model; if the key sequences and information retrieved from the data packets does not consistence with the software behavior model, it is determined that the transaction is an illegal electronic transactions, and the transaction is closed.

System and method for monitoring and verifying software behavior

A method for mining user cyclic patterns comprises steps: S: a user behavior recording module recording time of behaviors of each user; S: a user behavior time sequence generation module performing batch processing to the behaviors of each user to generate a time sequence of the behaviors of the user by using day as time granularity; and S: a user behavior cyclic pattern acquisition module generating time sequence cyclic patterns from the time sequence according to frequent patterns.

System and method for mining user cycle mode

A construction and analysis system of touch screen user keypress behavior pattern, and an identity recognition method thereof. Data analysis is performed by using historical keypress information of inputting a password by using a soft keyboard, a corresponding neural network model is established and model calculation is performed to new to-be-detected data to recognize a user identity; the system consists of a user data acquisition module, a data preprocessing module, a model training module and a user identity authentication module; the user data acquisition module is responsible for acquiring time sequence information, pressure and contact area information; the data preprocessing module is responsible for acquiring data, removing dirty data and normalizing to the data; the model training module is responsible for analysing input patterns and establishing models; and the user identity authentication module is for performing model calculation to new to-be-detected data to recognize user identities and improve the security of user account passwords.

Touch-screen user key-press behavior pattern construction and analysis system and identity recognition method thereof

A clustering coefficient-based adaptive clustering method, according to the categories of extracted data point pairs and the magnitude of association relations between data points extracted each time, determining to which category the data points belong, and the number of the categories, and establishing association relations between the data points and association relations between the categories; pre-segmenting each category and calculating the intra-category similarities of two sub-categories and inter-category similarities of the two sub-categories, judging whether the two pre-segmented sub-categories satisfy a segmentation condition, if so, then accepting the pre-segmentation; if not, then canceling the pre-segmentation; calculating the intra-category similarities and inter-category similarities of two categories having an association relation, judging whether the two categories satisfy a merging condition, if so, then merging the two categories to generate a new category; if not, then abandoning the merging.

Clustering coefficient-based adaptive clustering method and system

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Shanghai, China

Chungang Yan