Biblio

Cross site scripting (XSS) attack is one of the attacks on the web. It brings session hijack with HTTP cookies, information collection with fake HTML input form and phishing with dummy sites. As a countermeasure of XSS attack, machine learning has attracted a lot of attention. There are existing researches in which SVM, Random Forest and SCW are used for the detection of the attack. However, in the researches, there are problems that the size of data set is too small or unbalanced, and that preprocessing method for vectorization of strings causes misclassification. The highest accuracy of the classification was 98% in existing researches. Therefore, in this paper, we improved the preprocessing method for vectorization by using word2vec to find the frequency of appearance and co-occurrence of the words in XSS attack scripts. Moreover, we also used a large data set to decrease the deviation of the data. Furthermore, we evaluated the classification results with two procedures. One is an inappropriate procedure which some researchers tend to select by mistake. The other is an appropriate procedure which can be applied to an attack detection filter in the real environment.

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.

In order to effectively do network security protection, detecting system vulnerabilities becomes an indispensable process. Here, the vulnerability detection module with three functions is assembled into a device, and a composite detection tool with multiple functions is proposed to deal with some frequent vulnerabilities. The tool includes a total of three types of vulnerability detection, including cross-site scripting attacks, SQL injection, and directory traversal. First, let's first introduce the principle of each type of vulnerability; then, introduce the detection method of each type of vulnerability; finally, detail the defenses of each type of vulnerability. The benefits are: first, the cost of manual testing is eliminated; second, the work efficiency is greatly improved; and third, the network is safely operated in the first time.

The presence of software vulnerabilities is a serious threat to any software project. Exploiting them can compromise system availability, data integrity, and confidentiality. Unfortunately, many open source projects go for years with undetected ready-to-exploit critical vulnerabilities. In this study, we investigate the presence of software vulnerabilities in open source projects and the factors that influence this presence. We analyzed the top 100 open source PHP applications in GitHub using a static analysis vulnerability scanner to examine how common software vulnerabilities are. We also discussed which vulnerabilities are most present and what factors contribute to their presence. We found that 27% of these projects are insecure, with a median number of 3 vulnerabilities per vulnerable project. We found that the most common type is injection vulnerabilities, which made 58% of all detected vulnerabilities. Out of these, cross-site scripting (XSS) was the most common and made 43.5% of all vulnerabilities found. Statistical analysis revealed that project activities like branching, pulling, and committing have a moderate positive correlation with the number of vulnerabilities in the project. Other factors like project popularity, number of releases, and number of issues had almost no influence on the number of vulnerabilities. We recommend that open source project owners should set secure code development guidelines for their project members and establish secure code reviews as part of the project's development process.

In this paper, we present a combinatorial testing methodology for automated black-box security testing of complex web applications. The focus of our work is the identification of Cross-site Scripting (XSS) vulnerabilities. We introduce a new modelling scheme for test case generation of XSS attack vectors consisting of locally optimized attack models. The modelling approach takes into account the response and behavior of the web application and is particularly efficient when used in conjunction with combinatorial testing. In addition to the modelling scheme, we present a research prototype of a security testing tool called XSSInjector, which executes attack vectors generated from our methodology against web applications. The tool also employs a newly developed test oracle for detecting XSS which allow us to precisely identify whether injected JavaScript is actually executed and thus eliminate false positives. Our testing methodology is sufficiently generic to be applied to any web application that returns HTML code. We describe the foundations of our approach and validate it via an extensive case study using a verification framework and real world web applications. In particular, we have found several new critical vulnerabilities in popular forum software, library management systems and gallery packages.

QR codes are the means to offer more direct and instant access to information. However, QR codes have shown their deficiency, being a very powerful attack vector, for example, to execute phishing attacks. In this study, we have proposed a solution that allows controlling access to the information offered by QR codes. Through a scanner designed in APP Inventor which has been called XSStudent, a system has been built that analyzes the URLs obtained and compares them with a previously trained system. This study was executed by means of a controlled attack to the users of the university who through a flyer with a QR code and a fictional link accessed an infected page with JavaScript code that allowed a successful cross-site scripting attack. The results indicate that 100% of the users are vulnerable to this type of attacks, so also, with our proposal, an attack executed in the universities using the Beef software would be totally blocked.

As the technology is growing rapidly, the development of systems and software are becoming more complex. For this reason, the security of software and web applications become more vulnerable. In the last two decades, the use of internet application and security hacking activities are on top of the glance. The organizations are having the biggest challenge that how to secure their web applications from the rapidly increasing cyber threats because the organization can't compromise the security of their sensitive information. Vulnerability Assessment and Penetration Testing techniques may help organizations to find security loopholes. The weakness can be the asset for the attacker if the organizations are not aware of this. Vulnerability Assessment and Penetration Testing helps an organization to cover the security loopholes and determine their security arrangements are working as per defined policies or not. To cover the tracks and mitigate the threats it is necessary to install security patches. This paper includes the survey on the current vulnerabilities, determination of those vulnerabilities, the methodology used for determination, tools used to determine the vulnerabilities to secure the organizations from cyber threat.

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.

This research conducted a security evaluation website with Penetration Testing terms. This Penetration testing is performed using the Man-In-The-Middle Attack method. This method is still widely used by hackers who are not responsible for performing Sniffing, which used for tapping from a targeted computer that aims to search for sensitive data. This research uses some penetration testing techniques, namely SQL Injection, XSS (Cross-site Scripting), and Brute Force Attack. Penetration testing in this study was conducted to determine the security hole (vulnerability), so the company will know about their weakness in their system. The result is 85% success for the penetration testing that finds the vulnerability on the website.

\textbackslashtextbackslashtextitBackground: JavaScript frameworks are widely used to create client-side and server-side parts of contemporary web applications. Vulnerabilities like cross-site scripting introduce significant risks in web applications.\textbackslashtextbackslash\textbackslashtextbackslash \textbackslashtextbackslashtextitAim: The goal of our study is to understand how the security features of a framework impact the security of the applications written using that framework.\textbackslashtextbackslash\textbackslashtextbackslash \textbackslashtextbackslashtextitMethod: In this paper, we present four locations in an application, relative to the framework being used, where a mitigation can be applied. We perform an empirical study of JavaScript applications that use the three most common template engines: Jade/Pug, EJS, and Angular. Using automated and manual analysis of each group of applications, we identify the number of projects vulnerable to cross-site scripting, and the number of vulnerabilities in each project, based on the framework used.\textbackslashtextbackslash\textbackslashtextbackslash \textbackslashtextbackslashtextitResults: We analyze the results to compare the number of vulnerable projects to the mitigation locations used in each framework and perform statistical analysis of confounding variables.\textbackslashtextbackslash\textbackslashtextbackslash \textbackslashtextbackslashtextitConclusions: The location of the mitigation impacts the application's security posture, with mitigations placed within the framework resulting in more secure applications.

An important ingredient for a successful recipe for solving machine learning problems is the availability of a suitable dataset. However, such a dataset may have to be extracted from a large unstructured and semi-structured data like programming code, scripts, and text. In this work, we propose a plug-in based, extensible feature extraction framework for which we have prototyped as a tool. The proposed framework is demonstrated by extracting features from two different sources of semi-structured and unstructured data. The semi-structured data comprised of web page and script based data whereas the other data was taken from email data for spam filtering. The usefulness of the tool was also assessed on the aspect of ease of programming.

This paper studies the principle of vulnerability generation and mechanism of cross-site scripting attack, designs a dynamic cross-site scripting vulnerabilities detection technique based on existing theories of black box vulnerabilities detection. The dynamic detection process contains five steps: crawler, feature construct, attacks simulation, results detection and report generation. Crawling strategy in crawler module and constructing algorithm in feature construct module are key points of this detection process. Finally, according to the detection technique proposed in this paper, a detection tool is accomplished in Linux using python language to detect web applications. Experiments were launched to verify the results and compare with the test results of other existing tools, analyze the usability, advantages and disadvantages of the detection method above, confirm the feasibility of applying dynamic detection technique to cross-site scripting vulnerabilities detection.

Web applications are now considered one of the common platforms to represent data and conducting service releases throughout the World Wide Web. A number of the most commonly utilised frameworks for web applications are written in PHP. They became main targets because a vast number of servers are running these applications throughout the world. This increase in web application utilisation has made it more attractive to both users and hackers. According to the latest web security reports and research, cross site scripting (XSS) is the most popular vulnerability in PHP web application. XSS is considered an injection type of attack, which results in the theft of sensitive data, cookies, and sessions. Several tools and approaches have focused on detecting this kind of vulnerability in PHP source code. However, it is still a current problem in PHP web applications. This paper describes the popularity of PHP technology among other technologies, and highlight the approaches used to detect the most common vulnerabilities on PHP web applications, which is XSS. In addition, the discussion and the conclusion with future direction of research within this domain are highlighted.

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.

The root cause of cross-site scripting(XSS) attack is that the JavaScript engine can't distinguish between the JavaScript code in Web application and the JavaScript code injected by attackers. Moving Target Defense (MTD) is a novel technique that aim to defeat attacks by frequently changing the system configuration so that attackers can't catch the status of the system. This paper describes the design and implement of a XSS defense method based on Moving Target Defense technology. This method adds a random attribute to each unsafe element in Web application to distinguish between the JavaScript code in Web application and the JavaScript code injected by attackers and uses a security check function to verify the random attribute, if there is no random attribute or the random attribute value is not correct in a HTML (Hypertext Markup Language) element, the execution of JavaScript code will be prevented. The experiment results show that the method can effectively prevent XSS attacks and have little impact on the system performance.

While because the range of web users have increased exponentially, thus has the quantity of attacks that decide to use it for malicious functions. The vulnerability that has become usually exploited is thought as cross-site scripting (XSS). Cross-site Scripting (XSS) refers to client-side code injection attack whereby a malicious user will execute malicious scripts (also usually stated as a malicious payload) into a legitimate web site or web based application. XSS is amongst the foremost rampant of web based application vulnerabilities and happens once an internet based application makes use of un-validated or un-encoded user input at intervals the output it generates. In such instances, the victim is unaware that their data is being transferred from a website that he/she trusts to a different site controlled by the malicious user. In this paper we shall focus on type 1 or "non-persistent cross-site scripting". With non-persistent cross-site scripting, malicious code or script is embedded in a Web request, and then partially or entirely echoed (or "reflected") by the Web server without encoding or validation in the Web response. The malicious code or script is then executed in the client's Web browser which could lead to several negative outcomes, such as the theft of session data and accessing sensitive data within cookies. In order for this type of cross-site scripting to be successful, a malicious user must coerce a user into clicking a link that triggers the non-persistent cross-site scripting attack. This is usually done through an email that encourages the user to click on a provided malicious link, or to visit a web site that is fraught with malicious links. In this paper it will be discussed and elaborated as to how attack surfaces related to type 1 or "non-persistent cross-site scripting" attack shall be reduced using secure development life cycle practices and techniques.

Cross-site scripting (XSS) is a scripting attack targeting web applications by injecting malicious scripts into web pages. Blind XSS is a subset of stored XSS, where an attacker blindly deploys malicious payloads in web pages that are stored in a persistent manner on target servers. Most of the XSS detection techniques used to detect the XSS vulnerabilities are inadequate to detect blind XSS attacks. In this research, we present machine learning based approach to detect blind XSS attacks. Testing results help to identify malicious payloads that are likely to get stored in databases through web applications.

Nowadays, Cross Site Scripting (XSS) is one of the major threats to Web applications. Since it's known to the public, XSS vulnerability has been in the TOP 10 Web application vulnerabilities based on surveys published by the Open Web Applications Security Project (OWASP). How to effectively detect and defend XSS attacks are still one of the most important security issues. In this paper, we present a novel approach to detect XSS attacks based on deep learning (called DeepXSS). First of all, we used word2vec to extract the feature of XSS payloads which captures word order information and map each payload to a feature vector. And then, we trained and tested the detection model using Long Short Term Memory (LSTM) recurrent neural networks. Experimental results show that the proposed XSS detection model based on deep learning achieves a precision rate of 99.5% and a recall rate of 97.9% in real dataset, which means that the novel approach can effectively identify XSS attacks.

We present a novel method for static analysis in which we combine data-flow analysis with machine learning to detect SQL injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities in PHP applications. We assembled a dataset from the National Vulnerability Database and the SAMATE project, containing vulnerable PHP code samples and their patched versions in which the vulnerability is solved. We extracted features from the code samples by applying data-flow analysis techniques, including reaching definitions analysis, taint analysis, and reaching constants analysis. We used these features in machine learning to train various probabilistic classifiers. To demonstrate the effectiveness of our approach, we built a tool called WIRECAML, and compared our tool to other tools for vulnerability detection in PHP code. Our tool performed best for detecting both SQLi and XSS vulnerabilities. We also tried our approach on a number of open-source software applications, and found a previously unknown vulnerability in a photo-sharing web application.

The Web today is a growing universe of pages and applications teeming with interactive content. The security of such applications is of the utmost importance, as exploits can have a devastating impact on personal and economic levels. The number one programming language in Web applications is PHP, powering more than 80% of the top ten million websites. Yet it was not designed with security in mind and, today, bears a patchwork of fixes and inconsistently designed functions with often unexpected and hardly predictable behavior that typically yield a large attack surface. Consequently, it is prone to different types of vulnerabilities, such as SQL Injection or Cross-Site Scripting. In this paper, we present an interprocedural analysis technique for PHP applications based on code property graphs that scales well to large amounts of code and is highly adaptable in its nature. We implement our prototype using the latest features of PHP 7, leverage an efficient graph database to store code property graphs for PHP, and subsequently identify different types of Web application vulnerabilities by means of programmable graph traversals. We show the efficacy and the scalability of our approach by reporting on an analysis of 1,854 popular open-source projects, comprising almost 80 million lines of code.

Social media plays an integral part in individual's everyday lives as well as for companies. Social media brings numerous benefits in people's lives such as to keep in touch with close ones and specially with relatives who are overseas, to make new friends, buy products, share information and much more. Unfortunately, several threats also accompany the countless advantages of social media. The rapid growth of the online social networking sites provides more scope for criminals and cyber-criminals to carry out their illegal activities. Hackers have found different ways of exploiting these platform for their malicious gains. This research englobes some of the common threats on social media such as spam, malware, Trojan horse, cross-site scripting, industry espionage, cyber-bullying, cyber-stalking, social engineering attacks. The main purpose of the study to elaborates on phishing, malware and click-jacking attacks. The main purpose of the research, there is no particular research available on the forensic investigation for Facebook. There is no particular forensic investigation methodology and forensic tools available which can follow on the Facebook. There are several tools available to extract digital data but it's not properly tested for Facebook. Forensics investigation tool is used to extract evidence to determine what, when, where, who is responsible. This information is required to ensure that the sufficient evidence to take legal action against criminals.

Software systems nowadays communicate via a number of complex languages. This is often the cause of security vulnerabilities like arbitrary code execution, or injections. Whereby injections such as cross-site scripting are widely known from textual languages such as HTML and JSON that constantly gain more popularity. These systems use parsers to read input and unparsers write output, where these security vulnerabilities arise. Therefore correct parsing and unparsing of messages is of the utmost importance when developing secure and reliable systems. Part of the challenge developers face is to correctly encode data during unparsing and decode it during parsing. This paper presents McHammerCoder, an (un)parser and encoding generator supporting textual and binary languages. Those (un)parsers automatically apply the generated encoding, that is derived from the language's grammar. Therefore manually defining and applying encoding is not required to effectively prevent injections when using McHammerCoder. By specifying the communication language within a grammar, McHammerCoder provides developers with correct input and output handling code for their custom language.

Web Application becomes the leading solution for the utilization of systems that need access globally, distributed, cost-effective, as well as the diversity of the content that can run on this technology. At the same time web application security have always been a major issue that must be considered due to the fact that 60% of Internet attacks targeting web application platform. One of the biggest impacts on this technology is Cross Site Scripting (XSS) attack, the most frequently occurred and are always in the TOP 10 list of Open Web Application Security Project (OWASP). Vulnerabilities in this attack occur in the absence of checking, testing, and the attention about secure coding practices. There are several alternatives to prevent the attacks that associated with this threat. Network Intrusion Detection System can be used as one solution to prevent the influence of XSS Attack. This paper investigates the XSS attack recognition and detection using regular expression pattern matching and a preprocessing method. Experiments are conducted on a testbed with the aim to reveal the behaviour of the attack.

In recent years, with the advances in JavaScript engines and the adoption of HTML5 APIs, web applications begin to show a tendency to shift their functionality from the server side towards the client side, resulting in dense and complex interactions with HTML documents using the Document Object Model (DOM). As a consequence, client-side vulnerabilities become more and more prevalent. In this paper, we focus on DOM-sourced Cross-site Scripting (XSS), which is a kind of severe but not well-studied vulnerability appearing in browser extensions. Comparing with conventional DOM-based XSS, a new attack surface is introduced by DOM-sourced XSS where the DOM could become a vulnerable source as well besides common sources such as URLs and form inputs. To discover such vulnerability, we propose a detecting framework employing hybrid analysis with two phases. The first phase is the lightweight static analysis consisting of a text filter and an abstract syntax tree parser, which produces potential vulnerable candidates. The second phase is the dynamic symbolic execution with an additional component named shadow DOM, generating a document as a proof-of-concept exploit. In our large-scale real-world experiment, 58 previously unknown DOM-sourced XSS vulnerabilities were discovered in user scripts of the popular browser extension Greasemonkey.

Taint analysis has been used in numerous scripting languages such as Perl and Ruby to defend against various form of code injection attacks, such as cross-site scripting (XSS) and SQL-injection. However, most taint analysis systems simply fail when tainted information is used in a possibly unsafe manner. In this paper, we explore how precise taint tracking can be used in order to secure web content. Rather than simply crashing, we propose that a library-writer defined sanitization function can instead be used on the tainted portions of a string. With this approach, library writers or framework developers can design their tools to be resilient, even if inexperienced developers misuse these libraries in unsafe ways. In other words, developer mistakes do not have to result in system crashes to guarantee security. We implement both coarse-grained and precise taint tracking in JavaScript, and show how our precise taint tracking API can be used to defend against SQL injection and XSS attacks. We further evaluate the performance of this approach, showing that precise taint tracking involves an overhead of approximately 22%.