Biblio

In the Web 2.0 era, user interaction makes Web application more diverse, but brings threats, among which XSS vulnerability is the common and pernicious one. In order to promote the efficiency of XSS detection, this paper investigates the parameter characteristics of malicious XSS attacks. We identify whether a parameter is malicious or not through detecting user input parameters with SVM algorithm. The original malicious XSS parameters are deformed by DQN algorithm for reinforcement learning for rule-based WAF to be anti-anti-virus. Based on this method, we can identify whether a specific WAF is secure. The above model creates a more efficient automatic XSS detection tool and a more targeted automatic anti-anti-virus payload generation tool. This paper also explores the automatic generation of XSS attack codes with RNN LSTM algorithm.

To efficiently provide the ancient ceramic composition analysis and testing services, it is necessary to efficiently classify the ancient ceramics in ceramic cloud service platform. In this paper, we get the 8 kinds of major chemical contents of the body and glaze in each sample according to analyze 35 samples. After establishing of the classification model of two samples, the results indicate: as long as choosing SVM algorithm correctly, the classification results of body and glaze samples will be quite ideal, and the support vector machine is a very valuable new method which can process ancient porcelains data.