Biblio

For a long time, online attacks were regarded to pose a severe threat to web - based applications, websites, and clients. It can bypass authentication methods, steal sensitive information from datasets and clients, and also gain ultimate authority of servers. A variety of ways for safeguarding online apps have been developed and used to deal the website risks. Based on the studies about the intersection of cybersecurity and machine learning, countermeasures for identifying typical web assaults have recently been presented (ML). In order to establish a better understanding on this essential topic, it is necessary to study ML methodologies, feature extraction techniques, evaluate datasets, and performance metrics utilised in a systematic manner. In this paper, we go through web security flaws like SQLi, XSS, malicious URLs, phishing attacks, path traversal, and CMDi in detail. We also go through the existing security methods for detecting these threats using machine learning approaches for URL classification. Finally, we discuss potential research opportunities for ML and DL-based techniques in this category, based on a thorough examination of existing solutions in the literature.

Synthetic static code analysis test suites are important to test the basic functionality of tools. We present a framework that uses different source code patterns to generate Cross Site Scripting and SQL injection test cases. A decision tree is used to determine if the test cases are vulnerable. The test cases are split into two test suites. The first test suite contains 258,432 test cases that have influence on the decision trees. The second test suite contains 20 vulnerable test cases with different data flow patterns. The test cases are scanned with two commercial static code analysis tools to show that they can be used to benchmark and identify problems of static code analysis tools. Expert interviews confirm that the decision tree is a solid way to determine the vulnerable test cases and that the test suites are relevant.

Cross-Site scripting (XSS) is a common class of vulnerabilities in the domain of web applications. As it re-mains prevalent despite continued efforts by practitioners and researchers, site operators often seek to protect their assets using web application firewalls (WAFs). These systems employ filtering mechanisms to intercept and reject requests that may be suitable to exploit XSS flaws and related vulnerabilities such as SQL injections. However, they generally do not offer complete protection and can often be bypassed using specifically crafted exploits. In this work, we evaluate the effectiveness of WAFs to detect XSS exploits. We develop an attack grammar and use a combinatorial testing approach to generate attack vectors. We compare our vectors with conventional counterparts and their ability to bypass different WAFs. Our results show that the vectors generated with combinatorial testing perform equal or better in almost all cases. They further confirm that most of the rule sets evaluated in this work can be bypassed by at least one of these crafted inputs.

The accessibility of the internet and mobile platforms has risen dramatically due to digital technology innovations. Web applications have opened up a variety of market possibilities by supplying consumers with a wide variety of digital technologies that benefit from high accessibility and functionality. Around the same time, web application protection continues to be an important challenge on the internet, and security must be taken seriously in order to secure confidential data. The threat is caused by inadequate validation of user input information, software developed without strict adherence to safety standards, vulnerability of reusable software libraries, software weakness, and so on. Through abusing a website's vulnerability, introduers are manipulating the user's information in order to exploit it for their own benefit. Then introduers inject their own malicious code, stealing passwords, manipulating user activities, and infringing on customers' privacy. As a result, information is leaked, applications malfunction, confidential data is accessed, etc. To mitigate the aforementioned issues, stacking ensemble based classifier model for Cross-site scripting (XSS) attack detection is proposed. Furthermore, the stacking ensembles technique is used in combination with different machine learning classification algorithms like k-Means, Random Forest and Decision Tree as base-learners to reliably detect XSS attack. Logistic Regression is used as meta-learner to predict the attack with greater accuracy. The classification algorithms in stacking model explore the problem in their own way and its results are given as input to the meta-learner to make final prediction, thus improving the overall detection accuracy of XSS attack in stacking than the individual models. The simulation findings demonstrate that the proposed model detects XSS attack successfully.

With tons of efforts spent on its mitigation, Cross-site scripting (XSS) remains one of the most prevalent security threats on the internet. Decades of exploitation and remediation demonstrated that code inspection and testing alone does not eliminate XSS vulnerabilities in complex web applications with a high degree of confidence. This paper introduces Google's secure-by-design engineering paradigm that effectively prevents DOM-based XSS vulnerabilities in large-scale web development. Our approach, named API hardening, enforces a series of company-wide secure coding practices. We provide a set of secure APIs to replace native DOM APIs that are prone to XSS vulnerabilities. Through a combination of type contracts and appropriate validation and escaping, the secure APIs ensure that applications based thereon are free of XSS vulnerabilities. We deploy a simple yet capable compile-time checker to guarantee that developers exclusively use our hardened APIs to interact with the DOM. We make various of efforts to scale this approach to tens of thousands of engineers without significant productivity impact. By offering rigorous tooling and consultant support, we help developers adopt the secure coding practices as seamlessly as possible. We present empirical results showing how API hardening has helped reduce the occurrences of XSS vulnerabilities in Google's enormous code base over the course of two-year deployment.

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.

SQL Injection and Cross-Site Scripting are the two most common attacks in database-based web applications. In this paper we propose a system to detect different types of SQL injection and XSS attacks associated with a web application, without the existence of any firewall, while significantly reducing the network overhead. We use properly modifications of the Nginx Reverse Proxy protocols and Suricata NIDS/ IPS rules. Pure work has been done from other researchers based on the capabilities of Nginx and Suricata and our approach with the experimental results provided in the paper demonstrate the efficiency of our system.

E-commerce, ticket booking, banking, and other web-based applications that deal with sensitive information, such as passwords, payment information, and financial information, are widespread. Some web developers may have different levels of understanding about securing an online application. The two vulnerabilities identified by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List are SQL injection and Cross-site Scripting (XSS). Because of these two vulnerabilities, an attacker can take advantage of these flaws and launch harmful web-based actions. Many published articles concentrated on a binary classification for these attacks. This article developed a new approach for detecting SQL injection and XSS attacks using deep learning. SQL injection and XSS payloads datasets are combined into a single dataset. The word-embedding technique is utilized to convert the word’s text into a vector. Our model used BiLSTM to auto feature extraction, training, and testing the payloads dataset. BiLSTM classified the payloads into three classes: XSS, SQL injection attacks, and normal. The results showed great results in classifying payloads into three classes: XSS attacks, injection attacks, and non-malicious payloads. BiLSTM showed high performance reached 99.26% in terms of accuracy.

In our proposed work the web security has been enhanced using additional security code and an enhanced frame work. Administrator of site is required to specify the security code for particular date and time. On user end user would be capable to login and view authentic code allotted to them during particular time slot. This work would be better in comparison of tradition researches in order to prevent sql injection attack and cross script because proposed work is not just considering the security, it is also focusing on the performance of security system. This system is considering the lot of security dimensions. But in previous system there was focus either on sql injection or cross script. Proposed research is providing versatile security and is available with low time consumption with less probability of unauthentic access.

Web technologies are typically built with time constraints and security vulnerabilities. Automatic software vulnerability scanners are common tools for detecting such vulnerabilities among software developers. It helps to illustrate the program for the attacker by creating a great deal of engagement within the program. SQL Injection and Cross-Site Scripting (XSS) are two of the most commonly spread and dangerous vulnerabilities in web apps that cause to the user. It is very important to trust the findings of the site vulnerability scanning software. Without a clear idea of the accuracy and the coverage of the open-source tools, it is difficult to analyze the result from the automatic vulnerability scanner that provides. The important to do a comparison on the key figure on the automated vulnerability scanners because there are many kinds of a scanner on the market and this comparison can be useful to decide which scanner has better performance in term of SQL Injection and Cross-Site Scripting (XSS) vulnerabilities. In this paper, a method by Jose Fonseca et al, is used to compare open-source automated vulnerability scanners based on detection coverage and a method by Yuki Makino and Vitaly Klyuev for precision rate. The criteria vulnerabilities will be injected into the web applications which then be scanned by the scanners. The results then are compared by analyzing the precision rate and detection coverage of vulnerability detection. Two leading open source automated vulnerability scanners will be evaluated. In this paper, the scanner that being utilizes is OW ASP ZAP and Skipfish for comparison. The results show that from precision rate and detection rate scope, OW ASP ZAP has better performance than Skipfish by two times for precision rate and have almost the same result for detection coverage where OW ASP ZAP has a higher number in high vulnerabilities.

Cross-site scripting (XSS) attack is a kind of code injection that allows an attacker to inject malicious scripts code into a trusted web application. When a user tries to request the injected web page, he is not aware that the malicious script code might be affecting his computer. Nowadays, attackers are targeting the web applications that holding a sensitive data (e.g., bank transaction, e-mails, healthcare, and e-banking) to steal users' information and gain full access to the data which make the web applications to be more vulnerable. In this research, we applied three approaches to find a solution to this most challenging attacks issues. In the first approach, we implemented Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM) algorithms to discover and classify XSS attack. In the second approach, we implemented the Content Security Policy (CSP) approach to detect XSS attacks in real-time. In the last approach, we propose a new approach that combines the Web Application Firewall (WAF), Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) to detect and prevent XSS attack in real-time. Our experiment results demonstrated the high performance of AI algorithms. The CSP approach shows the results for the detection system report in real-time. In the third approach, we got more expected system results that make our third model system a more powerful tool to address this research problem than the other two approaches.

Cross-site scripting (XSS) is a common security vulnerability found in web applications. DOM-based XSS, one of the variants, is becoming particularly more prevalent with the boom of single-page applications where most of the UI changes are achieved by modifying the DOM through in-browser scripting. It is very easy for developers to introduce XSS vulnerabilities into web applications since there are many ways for user-controlled, unsanitized input to flow into a Web API and get interpreted as HTML markup and JavaScript code. An emerging Web API proposal called Trusted Types aims to prevent DOM XSS by making Web APIs secure by default. Different from other XSS mitigations that mostly focus on post-development protection, Trusted Types direct developers to write XSS-free code in the first place. A common concern when adopting a new security mechanism is how much effort is required to refactor existing code bases. In this paper, we report a case study on adopting Trusted Types in a well-established web framework. Our experience can help the web community better understand the benefits of making web applications compatible with Trusted Types, while also getting to know the related challenges and resolutions. We focused our work on Angular, which is one of the most popular web development frameworks available on the market.

On the current website, there are many undeniable conditions and there is the existence of new plot holes. If data link is normally extracted on each of the websites, it becomes difficult to evaluate each vulnerability, with tolls such as XS S, SQLI, and other such existing tools for vulnerability assessment. Integrated testing criteria for vulnerabilities are met. In addition, the response should be automated and systematic. The primary value of vulnerability Buffer will be made of predefined and self-formatted code written in python, and the software is automated to send reports to their respective users. The vulnerabilities are tried to be classified as accessible. OWASP is the main resource for developing and validating web security processes.

Black-box web application scanners are used to detect vulnerabilities in the web application without any knowledge of the source code. Recent research had shown their poor performance in detecting stored Cross-Site Scripting (XSS) and stored SQL Injection (SQLI). The detection efficiency of four black-box scanners on two testbeds, Wackopicko and Custom testbed Scanit (obtained from [5]), have been analyzed in this paper. The analysis showed that the scanners need to be improved for better detection of multi-step stored XSS and stored SQLI. This study involves the interaction between the selected scanners and the web application to measure their efficiency of inserting proper attack vectors in appropriate fields. The results of this research paper indicate that there is not much difference in terms of performance between open-source and commercial black-box scanners used in this research. However, it may depend on the policies and trust issues of the companies using them according to their needs. Some of the possible recommendations are provided to improve the detection rate of stored SQLI and stored XSS vulnerabilities in this paper. The study concludes that the state-of-the-art of automated black-box web application scanners in 2020 needs to be improved to detect stored XSS and stored SQLI more effectively.

Web browsers display content in the form of HTML, CSS and JavaScript retrieved from the world wide web. The loaded content is subject to the web security model and considered untrusted and potentially malicious. To complicate security matters, Firefox uses the same technologies to render its user interface as it does to render untrusted web content which blurs the distinction between the two privilege levels.Getting interactions between the two correct turns out to be complicated and has led to numerous real-world security vulnerabilities. We study those vulnerabilities to discover common threats and explain how we address them systematically to harden Firefox.

Today’s rapidly changing world, is observing fast development of QR-code and Blockchain technologies. It is worth noting that these technologies have also received a boost for sharing. The user gets the opportunity to receive / send funds, issue invoices for payment and transfer, for example, Bitcoin using QR-code. This paper discusses the security of using the symbiosis of Blockchain and QR-code technologies, and the vulnerabilities that arise in this case. The following vulnerabilities were considered: fake QR generators, stickers for cryptomats, phishing using QR-codes, create Malicious QR-Codes for Hack Phones and Other Scanners. The possibility of creating the following malicious QR codes while using the QRGen tool was considered: SQL Injections, XSS (Cross-Site Scripting), Command Injection, Format String, XXE (XML External Entity), String Fuzzing, SSI (Server-Side Includes) Injection, LFI (Local File Inclusion) / Directory Traversal.

Considering the amount of time we spend on the internet, web pages have evolved over a period of time with rapid progression and momentum. With such advancement, we find ourselves fronting a few hostile ideologies, breaching the security levels of webpages as such. The most hazardous of them all is XSS, known as Cross-Site Scripting, is one of the attacks which frequently occur in website-based applications. Cross-Site Scripting (XSS) attacks happen when malicious data enters a web application through an untrusted source. The spam attacks happen in the form of Wall posts, News feed, Message spam and mostly when a user is open to download content of webpages. This paper investigates the use of machine learning to build classifiers to allow the detection of XSS. Establishing our approach, we target the detection modus operandi of XSS attack via two features: URLs and JavaScript. To predict the level of XSS threat, we will be using four machine learning algorithms (SVM, KNN, Random forest and Logistic Regression). Proposing these classified algorithms, webpages will be branded as malicious or benign. After assessing and calculating the dataset features, we concluded that the Random Forest Classifier performed most accurately with the lowest False Positive Rate of 0.34. This precision will ensure a method much efficient to evaluate threatening XSS for the smooth functioning 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.

This article discusses the problem of detecting cross-site scripting (XSS) using machine learning methods. XSS is an attack in which malicious code is embedded on a page to interact with an attacker’s web server. The XSS attack ranks third in the ranking of key web application risks according to Open Source Foundation for Application Security (OWASP). This attack has not been studied for a long time. It was considered harmless. However, this is fallacious: the page or HTTP Cookie may contain very vulnerable data, such as payment document numbers or the administrator session token. Machine learning is a tool that can be used to detect XSS attacks. This article describes an experiment. As a result the model for detecting XSS attacks was created. Following machine learning algorithms are considered: the support vector method, the decision tree, the Naive Bayes classifier, and Logistic Regression. The accuracy of the presented methods is made a comparison.

Cross-Site Scripting, also called as XSS, is a type of injection where malicious scripts are injected into trusted websites. When malicious code, usually in the form of browser side script, is injected using a web application to a different end user, an XSS attack is said to have taken place. Flaws which allows success to this attack is remarkably widespread and occurs anywhere a web application handles the user input without validating or encoding it. A study carried out by Symantic states that more than 50% of the websites are vulnerable to the XSS attack. Security engineers of Microsoft coined the term "Cross-Site Scripting" in January of the year 2000. But even if was coined in the year 2000, XSS vulnerabilities have been reported and exploited since the beginning of 1990's, whose prey have been all the (then) tech-giants such as Twitter, Myspace, Orkut, Facebook and YouTube. Hence the name "Cross-Site" Scripting. This attack could be combined with other attacks such as phishing attack to make it more lethal but it usually isn't necessary, since it is already extremely difficult to deal with from a user perspective because in many cases it looks very legitimate as it's leveraging attacks against our banks, our shopping websites and not some fake malicious website.

In accordance to the annual report by the Cisco 2018, web applications are exposed to several security vulnerabilities that are exploited by hackers in various ways. It is becoming more and more frequent, specific and sophisticated. Of all the vulnerabilities, more than 40% of attempts are performed via cross-site scripting (XSS). A number of methods have been postulated to examine such vulnerabilities. Therefore, this paper attempted to address an overview of one such vulnerability: the cookies in the XSS. The objective is to present an overview of the cookies, it's type, vulnerability, policies, discovering, protecting and their mitigation via different tools/methods and via cryptography, artificial intelligence techniques etc. While some future issues, directions, challenges and future research challenges were also being discussed.

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%.

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.