Cui Baojiang

Improved Plagiarism Detection Algorithm Based on Abstract Syntax Tree

Statements with conditionals are widely used in C, C++ and java, such as if and while statements and they are easy to plagiarize by adjusting the logical structure of the corresponding statements. However, the existing relative algorithms and tools cannot effectively detect code plagiarism of these statements. This paper puts forward an improved code plagiarism detection algorithm based on abstract syntax tree. The algorithm calculates the hash value for each node of the abstract syntax tree, and compares the hash values node by node. Based on this, it analyzes the if-statement plagiarism, as if-statements are representative in the statements with conditionals, and puts forward the corresponding detection schemes in order to detect plagiarism effectively. After that, with the results of many experiments, the algorithm is proved effective on detecting if-statement plagiarisms.

Sentence Similarity Based on Semantic Vector Model

Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information, structure information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database, How-net. The use of a lexical database enables our method to model human common sense knowledge. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows higher accuracy than other methods.

Reverse Analysis Method of Static XSS Defect Detection Technique Based on Database Query Language

Along with the wide use of web application, XSS vulnerability has become one of the most common security problems and caused many serious losses. In this paper, on the basis of database query language technique, we put forward a static analysis method of XSS defect detection of java web application by analyzing data flow reversely. This method first converts the JSP file to a Servlet file, and then uses the mock test method to generate calls for all Java code automatically for comprehensive analysis. Originated from the methods where XSS security defect may occur, we analyze the data flow reversely to detect XSS defect by judging whether it can be introduced by user input without filter. This reverse method has effectively reduced analyzing tasks which are necessary in forward ways. It was proved by experiments on artificially constructed Java web project with XSS flaws and some open source Java web projects, this method not only improved the efficiency of detection, but also improved the detection accuracy for XSS defect.