Biblio

With the progress of science and technology and the development of Internet technology, Internet technology has penetrated into various industries in today’s society. But this explosive growth is also troubling information security. Among them, XSS (cross-site scripting vulnerability) is one of the most influential vulnerabilities in Internet applications in recent years. Traditional network security detection technology is becoming more and more weak in the new network environment, and deep learning methods such as CNN and RNN can only learn the spatial or timing characteristics of data samples in a single way. In this paper, a generalized self-regression pretraining model XLNet and GRU XSS attack detection method is proposed, the self-regression pretrained model XLNet is introduced and combined with GRU to learn the time series and spatial characteristics of the data, and the generalization capability of the model is improved by using dropout. Faced with the increasingly complex and ever-changing XSS payload, this paper refers to the character-level convolution to establish a dictionary to encode the data samples, thus preserving the characteristics of the original data and improving the overall efficiency, and then transforming it into a two-dimensional spatial matrix to meet XLNet’s input requirements. The experimental results on the Github data set show that the accuracy of this method is 99.92 percent, the false positive rate is 0.02 percent, the accuracy rate is 11.09 percent higher than that of the DNN method, the false positive rate is 3.95 percent lower, and other evaluation indicators are better than GRU, CNN and other comparative methods, which can improve the detection accuracy and system stability of the whole detection system. This multi-model fusion method can make full use of the advantages of each model to improve the accuracy of system detection, on the other hand, it can also enhance the stability of the system.

Cross-site scripting (XSS) is one of the main threats of Web applications, which has great harm. How to effectively detect and defend against XSS attacks has become more and more important. Due to the malicious obfuscation of attack codes and the gradual increase in number, the traditional XSS detection methods have some defects such as poor recognition of malicious attack codes, inadequate feature extraction and low efficiency. Therefore, we present a novel approach to detect XSS attacks based on the attention mechanism of Long Short-Term Memory (LSTM) recurrent neural network. First of all, the data need to be preprocessed, we used decoding technology to restore the XSS codes to the unencoded state for improving the readability of the code, then we used word2vec to extract XSS payload features and map them to feature vectors. And then, we improved the LSTM model by adding attention mechanism, the LSTM-Attention detection model was designed to train and test the data. We used the ability of LSTM model to extract context-related features for deep learning, the added attention mechanism made the model extract more effective features. Finally, we used the classifier to classify the abstract features. Experimental results show that the proposed XSS detection model based on LSTM-Attention achieves a precision rate of 99.3% and a recall rate of 98.2% in the actually collected dataset. Compared with traditional machine learning methods and other deep learning methods, this method can more effectively identify XSS attacks.

Cross-site scripting (XSS) is an often-occurring major attack that developers should consider when developing web applications. We develop a system that can provide practical exercises for learning how to create web applications that are secure against XSS. Our system utilizes free software and virtual machines, allowing low-cost, safe, and practical exercises. By using two virtual machines as the web server and the attacker host, the learner can conduct exercises demonstrating both XSS countermeasures and XSS attacks. In our system, learners use a web browser to learn and perform exercises related to XSS. Experimental evaluations confirm that the proposed system can support learning of XSS countermeasures.

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.

In this paper, we present an overview of the problems associated with the cross-site scripting (XSS) in the graphical content of web applications. The brief analysis of vulnerabilities for graphical files and factors responsible for making SVG images vulnerable to XSS attacks are discussed. XML treatment methods and their practical testing are performed. As a result, the set of rules for protecting the graphic content of the websites and prevent XSS vulnerabilities are proposed.

Today, maintaining the security of the web application is of great importance. Sites Intermediate Script (XSS) is a security flaw that can affect web applications. This error allows an attacker to add their own malicious code to HTML pages that are displayed to the user. Upon execution of the malicious code, the behavior of the system or website can be completely changed. The XSS security vulnerability is used by attackers to steal the resources of a web browser such as cookies, identity information, etc. by adding malicious Java Script code to the victim's web applications. Attackers can use this feature to force a malicious code worker into a Web browser of a user, since Web browsers support the execution of embedded commands on web pages to enable dynamic web pages. This work has been proposed as a technique to detect and prevent manipulation that may occur in web sites, and thus to prevent the attack of Site Intermediate Script (XSS) attacks. Ayrica has developed four different languages that detect XSS explanations with Asp.NET, PHP, PHP and Ruby languages, and the differences in the detection of XSS attacks in environments provided by different programming languages.

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.

This paper presents the design and implementation of an information flow tracking framework based on code rewrite to prevent sensitive information leaks in browsers, combining the ideas of taint and information flow analysis. Our system has two main processes. First, it abstracts the semantic of JavaScript code and converts it to a general form of intermediate representation on the basis of JavaScript abstract syntax tree. Second, the abstract intermediate representation is implemented as a special taint engine to analyze tainted information flow. Our approach can ensure fine-grained isolation for both confidentiality and integrity of information. We have implemented a proof-of-concept prototype, named JSTFlow, and have deployed it as a browser proxy to rewrite web applications at runtime. The experiment results show that JSTFlow can guarantee the security of sensitive data and detect XSS attacks with about 3x performance overhead. Because it does not involve any modifications to the target system, our system is readily deployable in practice.
 

The inappropriate use of features intended to improve usability and interactivity of web applications has resulted in the emergence of various threats, including Cross-Site Scripting(XSS) attacks. In this work, we developed ETSS Detector, a generic and modular web vulnerability scanner that automatically analyzes web applications to find XSS vulnerabilities. ETSS Detector is able to identify and analyze all data entry points of the application and generate specific code injection tests for each one. The results shows that the correct filling of the input fields with only valid information ensures a better effectiveness of the tests, increasing the detection rate of XSS attacks.
 

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.