National University of Defense Technology

Xianqiang Zhu

Zhaoyun Ding

Cheng Zhu

Bin Liu

Hang Zhang

Yi Liu

Weike Liu

Pengtao Fu

Deqi Cao

Yun Zhou

Fei Wang

Songping Huang

A cross-site scripting (XSS) risk analysis method based on a Bayesian network and a STRIDE model includes: constructing an XSS attack-related STRIDE threat model of a network information release system; obtaining a network structure of a Bayesian network-based XSS attack risk analysis model based on the STRIDE model; obtaining prior probabilities of all nodes in the network structure of the Bayesian network-based XSS attack risk analysis model based on expert experience and a node ranking algorithm; obtaining a training dataset through simulation by using a rejection sampling algorithm or a direct sampling method; performing network training on the network structure of the Bayesian network-based XSS attack risk analysis model by using the training dataset to obtain the Bayesian network-based XSS attack risk analysis model; and reasoning a Bayesian network to obtain a quantitative analysis result of an XSS attack risk of the network information release system.

Cross-site scripting (XSS) risk analysis method and apparatus based on bayesian network and stride model

The disclosure relates to a method and device for identifying unknown traffic data based on a dynamic network environment. The method includes following steps. The known traffic in the network data is classified by using the known network traffic classification model, then the preliminary determination is performed according to a classification prediction result, network data preliminarily determined as the unknown traffic data is classified by using the adaptive clustering method, and then respective classes are identified by using a similarity coefficient estimation method so as to identify the classes of the malicious traffic and the normal traffic, that is, to further identify and learn the unknown traffic data, and transform it into known traffic data, and then the known network traffic classification model is trained and updated again with the new known traffic data.

Method and device for identifying unknown traffic data based dynamic network environment

Disclosed are a network traffic analysis method and a device based on multi-source network traffic data. The method includes: deploying a pre-training classifier pool in a network stream data source; receiving multi-source network stream data at a current moment for each data source, classifying the multi-source network stream data through an online classifier, performing feature processing and transformation on data collected by each network stream data source at each preset time interval, and transmitting processed traffic data features and a feature transformation matrix to a traffic drift detection module. The traffic drift detection module contains historical concept data to detect a concept drift according to the traffic data features, the feature transformation matrix and the historical concept data; if the concept drift is detected, the online classifier deployed by multiple sources is reset. This method is used for continuous real-time and accurate analysis of the multi-source network traffic data.

Network traffic analysis method and device based on multi-source network traffic data

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Changsha, China

Xianqiang Zhu