Biblio

High frequency of network security incidents has also brought a lot of negative effects and even huge economic losses to countries, enterprises and individuals in recent years. Therefore, more and more attention has been paid to the problem of network security. In order to evaluate the newly included vulnerability text information accurately, and to reduce the workload of experts and the false negative rate of the traditional method. Multiple deep learning methods for vulnerability text classification evaluation are proposed in this paper. The standard Cross Site Scripting (XSS) vulnerability text data is processed first, and then classified using three kinds of deep neural networks (CNN, LSTM, TextRCNN) and one kind of traditional machine learning method (XGBoost). The dropout ratio of the optimal CNN network, the epoch of all deep neural networks and training set data were tuned via experiments to improve the fit on our target task. The results show that the deep learning methods evaluate vulnerability risk levels better, compared with traditional machine learning methods, but cost more time. We train our models in various training sets and test with the same testing set. The performance and utility of recurrent convolutional neural networks (TextRCNN) is highest in comparison to all other methods, which classification accuracy rate is 93.95%.

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.

XSS is one of the common vulnerabilities in web applications. Many black-box testing tools may collect a large number of payloads and traverse them to find a payload that can be successfully injected, but they are not very efficient. And previous research has paid less attention to how to improve the efficiency of black-box testing to detect XSS vulnerability. To improve the efficiency of testing, we develop an XSS testing tool. It collects 6128 payloads and uses a headless browser to detect XSS vulnerability. The tool can discover XSS vulnerability quickly with the ART(Adaptive Random Testing) method. We conduct an experiment using 3 extensively adopted open source vulnerable benchmarks and 2 actual websites to evaluate the ART method. The experimental results indicate that the ART method can effectively improve the fuzzing method by more than 27.1% in reducing the number of attempts before accomplishing a successful injection.

Many web applications are vulnerable to Cross Site Scripting (XSS) attacks enabling attackers to steal sensitive information and commit frauds. Much research in this area have focused on detecting vulnerable web pages using static and dynamic program analysis. The best practice to prevent XSS vulnerabilities is to encode untrusted dynamic content. However, a common programming error is the use of a wrong type of encoder to sanitize untrusted data, leaving the application vulnerable. We propose a new approach that can automatically fix this common type of XSS vulnerability in many situations. This approach is integrated into the software maintenance life cycle through unit testing. Vulnerable codes are refactored to reflect the suggested encoder and then verified using an attack evaluating mechanism to find a proper repair. Evaluation of this approach has been conducted on an open source medical record application with over 200 web pages written in JSP.

The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are automatically constructed out of each web page and then evaluated by a unit test execution framework. A grammar-based attack generator is used to automatically generate test inputs. We evaluate our approach on a large open source medical records application, demonstrating that we can detect many 0-day XSS vulnerabilities with very low false positives, and that the grammar-based attack generator has better test coverage than industry best practices.

Cross Site Scripting attack (XSS) is the computer security threat which allows the attacker to get access over the sensitive information, when the javaScript, VBScript, ActiveX, Flash or HTML which is embedded in the malicious XSS link gets executed. In this paper, we authors have discussed about various impacts of XSS, types of XSS, checked whether the site is vulnerable towards the XSS or not, discussed about various tools for examining the XSS vulnerability and summarizes the preventive measures against XSS.

Currently, dependence on web applications is increasing rapidly for social communication, health services, financial transactions and many other purposes. Unfortunately, the presence of cross-site scripting vulnerabilities in these applications allows malicious user to steals sensitive information, install malware, and performs various malicious operations. Researchers proposed various approaches and developed tools to detect XSS vulnerability from source code of web applications. However, existing approaches and tools are not free from false positive and false negative results. In this paper, we propose a taint analysis and defensive programming based HTML context-sensitive approach for precise detection of XSS vulnerability from source code of PHP web applications. It also provides automatic suggestions to improve the vulnerable source code. Preliminary experiments and results on test subjects show that proposed approach is more efficient than existing ones.