Biblio

The biometric system of access to information resources has been developed. The software and hardware complex are designed to protect information resources and personal data from unauthorized access using the principle of user authentication by fingerprints. In the developed complex, the traditional input of login and password was replaced by applying a finger to the fingerprint scanner. The system automatically recognizes the fingerprint and provides access to the information resource, provides encryption of personal data and automation of the authorization process on the web resource. The web application was implemented using the Bootstrap framework, the 000webhost web server, the phpMyAdmin database server, the PHP scripting language, the HTML hypertext markup language, along with cascading style sheets and embedded scripts (JavaScript), which created a full-fledged web-site and Google Chrome extension with the ability to integrate it into other systems. The structural schematic diagram was performed. The design of the device is offered. The algorithm of the program operation and the program of the device operation in the C language are developed.

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.

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.

Cryptocurrencies are the digital currencies designed to replace the regular cash money while taking place in our daily lives especially for the last couple of years. Mining cryptocurrencies are one of the popular ways to have them and make a profit due to unstable values in the market. This attracts attackers to utilize malware on internet users' computer resources, also known as cryptojacking, to mine cryptocurrencies. Cryptojacking started to be a major issue in the internet world. In this case, we developed MiNo, a web browser add-on application to detect these malicious mining activities running without the user's permission or knowledge. This add-on provides security and efficiency for the computer resources of the internet users. MiNo designed and developed with double-layer protection which makes it ahead of its competitors in the market.

Cryptojacking is the exploitation of victims' computer resources to mine for cryptocurrency using malicious scripts. It had become popular after 2017 when attackers started to exploit legal mining scripts, especially Coinhive scripts. Coinhive was actually a legal mining service that provided scripts and servers for in-browser mining activities. Nevertheless, over 10 million web users had been victims every month before the Coinhive shutdown that happened in Mar 2019. This paper explores the new era of the cryptojacking world after Coinhive discontinued its service. We aimed to see whether and how attackers continue cryptojacking, generate new malicious scripts, and developed new methods. We used a capable cryptojacking detector named CMTracker that proposed by Hong et al. in 2018. We automatically and manually examined 2770 websites that had been detected by CMTracker before the Coinhive shutdown. The results revealed that 99% of sites no longer continue cryptojacking. 1% of websites still run 8 unique mining scripts. By tracking these mining scripts, we detected 632 unique cryptojacking websites. Moreover, open-source investigations (OSINT) demonstrated that attackers still use the same methods. Therefore, we listed the typical patterns of cryptojacking. We concluded that cryptojacking is not dead after the Coinhive shutdown. It is still alive, but not as attractive as it used to be.

Web technology has evolved to offer 360-degree immersive browsing experiences. This new technology, called WebVR, enables virtual reality by rendering a three-dimensional world on an HTML canvas. Unfortunately, there exists no browser-supported way of sharing this canvas between different parties. As a result, third-party library providers with ill intent (e.g., stealing sensitive information from end-users) can easily distort the entire WebVR site. To mitigate the new threats posed in WebVR, we propose CanvasMirror, which allows publishers to specify the behaviors of third-party libraries and enforce this specification. We show that CanvasMirror effectively separates the third-party context from the host origin by leveraging the privilege separation technique and safely integrates VR contents on a shared canvas.

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.

Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.

Over the past few years, we have been witnessing the rise of misinformation on the Internet. People fall victims of fake news continuously, and contribute to their propagation knowingly or inadvertently. Many recent efforts seek to reduce the damage caused by fake news by identifying them automatically with artificial intelligence techniques, using signals from domain flag-lists, online social networks, etc. In this work, we present Check-It, a system that combines a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, while respecting user privacy. In this paper, we present the design, implementation and performance evaluation of Check-It. Experimental results show that it outperforms state-of-the-art methods on commonly-used datasets.

Cryptojacking (also called malicious cryptocurrency mining or cryptomining) is a new threat model using CPU resources covertly “mining” a cryptocurrency in the browser. The impact is a surge in CPU Usage and slows the system performance. In this research, in-browsercryptojacking mitigation has been built as an extension in Google Chrome using Taint analysis method. The method used in this research is attack modeling with abuse case using the Man-In-The-Middle (MITM) attack as a testing for mitigation. The proposed model is designed so that users will be notified if a cryptojacking attack occurs. Hence, the user is able to check the script characteristics that run on the website background. The results of this research show that the taint analysis is a promising method to mitigate cryptojacking attacks. From 100 random sample websites, the taint analysis method can detect 19 websites that are infcted by cryptojacking.

With the recent boom in the cryptocurrency market, hackers have been on the lookout to find novel ways of commandeering users' machine for covert and stealthy mining operations. In an attempt to expose such under-the-hood practices, this paper explores the issue of browser cryptojacking, whereby miners are secretly deployed inside browser code without the knowledge of the user. To this end, we analyze the top 50k websites from Alexa and find a noticeable percentage of sites that are indulging in this exploitative exercise often using heavily obfuscated code. Furthermore, mining prevention plug-ins, such as NoMiner, fail to flag such cleverly concealed instances. Hence, we propose a machine learning solution based on hardware-assisted profiling of browser code in real-time. A fine-grained micro-architectural footprint allows us to classify mining applications with \textbackslashtextgreater99% accuracy and even flags them if the mining code has been heavily obfuscated or encrypted. We build our own browser extension and show that it outperforms other plug-ins. The proposed design has negligible overhead on the user's machine and works for all standard off-the-shelf CPUs.

Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websites' concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flow-based network traffic features, the accuracy grows significantly.

Bitcoin is popular not only with consumers, but also with cybercriminals (e.g., in ransomware and online extortion, and commercial online child exploitation). Given the potential of Bitcoin to be involved in a criminal investigation, the need to have an up-to-date and in-depth understanding on the forensic acquisition and analysis of Bitcoins is crucial. However, there has been limited forensic research of Bitcoin in the literature. The general focus of existing research is on postmortem analysis of specific locations (e.g. wallets on mobile devices), rather than a forensic approach that combines live data forensics and postmortem analysis to facilitate the identification, acquisition, and analysis of forensic traces relating to the use of Bitcoins on a system. Hence, the latter is the focus of this paper where we present an open source tool for live forensic and postmortem analysing automatically. Using this open source tool, we describe a list of target artifacts that can be obtained from a forensic investigation of popular Bitcoin clients and Web Wallets on different web browsers installed on Windows 7 and Windows 10 platforms.

Most Web sites rely on resources hosted by third parties such as CDNs. Third parties may be compromised or coerced into misbehaving, e.g. delivering a malicious script or stylesheet. Unexpected changes to resources hosted by third parties can be detected with the Subresource Integrity (SRI) mechanism. The focus of SRI is on scripts and stylesheets. Web fonts cannot be secured with that mechanism under all circumstances. The first contribution of this paper is to evaluates the potential for attacks using malicious fonts. With an instrumented browser we find that (1) more than 95% of the top 50,000 Web sites of the Tranco top list rely on resources hosted by third parties and that (2) only a small fraction employs SRI. Moreover, we find that more than 60% of the sites in our sample use fonts hosted by third parties, most of which are being served by Google. The second contribution of the paper is a proof of concept of a malicious font as well as a tool for automatically generating such a font, which targets security-conscious users who are used to verifying cryptographic fingerprints. Software vendors publish such fingerprints along with their software packages to allow users to verify their integrity. Due to incomplete SRI support for Web fonts, a third party could force a browser to load our malicious font. The font targets a particular cryptographic fingerprint and renders it as a desired different fingerprint. This allows attackers to fool users into believing that they download a genuine software package although they are actually downloading a maliciously modified version. Finally, we propose countermeasures that could be deployed to protect the integrity of Web fonts.

All popular browsers support browser extensions. They are small software module for customizing web browsers. It provides extra features like user interface modifications, ad blocking, cookie management and so on. As features increase, security becomes more difficult. The impact of malicious browser extensions is also enormous. More than 1 million Chrome users got affected by extensions from Chrome store itself. [1] The risk further increases with offline extension installations. The privileges browser extensions have, pave the path for many kinds of attacks. Replay attack and session hijacking are two of these attacks we are dealing here. Here we propose a defence system based on dynamic encrypted cookies to defend these attacks. We use cookies as token for continuous authentication, which protects entire communication. Static cookies are prone for session hijacking, and therefore we use dynamic cookies which are sealed with encryption. It also protects from replay attack by changing itself, making previous message obsolete. This essentially solves both of the problems.

Modern Browsers have become sophisticated applications, providing a portal to the web. Browsers host a complex mix of interpreters such as HTML and JavaScript, allowing not only useful functionality but also malicious activities, known as browser-hijacking. These attacks can be particularly difficult to detect, as they usually operate within the scope of normal browser behaviour. CryptoJacking is a form of browser-hijacking that has emerged as a result of the increased popularity and profitability of cryptocurrencies, and the introduction of new cryptocurrencies that promote CPU-based mining. This paper proposes MANiC (Multi-step AssessmeNt for Crypto-miners), a system to detect CryptoJacking websites. It uses regular expressions that are compiled in accordance with the API structure of different miner families. This allows the detection of crypto-mining scripts and the extraction of parameters that could be used to detect suspicious behaviour associated with CryptoJacking. When MANiC was used to analyse the Alexa top 1m websites, it detected 887 malicious URLs containing miners from 11 different families and demonstrated favourable results when compared to related CryptoJacking research. We demonstrate that MANiC can be used to provide insights into this new threat, to identify new potential features of interest and to establish a ground-truth dataset, assisting future research.

The safety of web browsers is essential to the privacy of Internet users and the security of their computing systems. In the last few years, there have been several cyber attacks geared towards compromising surfers' data and systems via exploiting browser-based vulnerabilities. Android and a number of mobile operating systems have been supporting a UI component called WebView, which can be embedded in any mobile application to render the web contents. Yet, this mini-browser component has been found to be vulnerable to various kinds of attacks. For instance, an attacker in her WebView-Embedded app can inject malicious JavaScripts into the WebView to modify the web contents or to steal user's input values. This kind of attack is particularly challenging due to the full control of attackers over the content of the loaded pages. In this paper, we are proposing and testing a server-side moving target defense technique to counter the risk of JavaScript injection attacks on mobile WebViews. The solution entails creating redundant HTML forms, randomizing their attributes and values, and asserting stealthy prompts for the user data. The solution does not dictate any changes to the browser or applications codes, neither it requires key sharing with benign clients. The results of our performance and security analysis suggest that our proposed approach protects the confidentiality and integrity of user input values with minimum overhead.

Certificate Transparency (CT) is an emerging system for enabling the rapid discovery of malicious or misissued certificates. Initially standardized in 2013, CT is now finally beginning to see widespread support. Although CT provides desirable security benefits, web browsers cannot begin requiring all websites to support CT at once, due to the risk of breaking large numbers of websites. We discuss challenges for deployment, analyze the adoption of CT on the web, and measure the error rates experienced by users of the Google Chrome web browser. We find that CT has so far been widely adopted with minimal breakage and warnings. Security researchers often struggle with the tradeoff between security and user frustration: rolling out new security requirements often causes breakage. We view CT as a case study for deploying ecosystem-wide change while trying to minimize end user impact. We discuss the design properties of CT that made its success possible, as well as draw lessons from its risks and pitfalls that could be avoided in future large-scale security deployments.

HTTPS provides authentication, data confidentiality, and integrity for secure web applications in the Internet. In order to establish secure connections with the target website but not a man-in-the-middle or impersonation attacker, a browser shows security warnings to users, when different HTTPS errors happen (e.g., it fails to build a valid certificate chain, or the certificate subject does not match the domain visited). Each browser implements its own design of warnings on HTTPS errors, to balance security and usability. This paper presents a list of common HTTPS errors, and we investigate the browser behaviors on each error. Our study discloses browser defects on handling HTTPS errors in terms of cryptographic algorithm, certificate verification, name validation, HPKP, and HSTS.

The aim of this work is to create a new style web browser. The other web browsers can have safety issues and have many ads and popups. The other web browsers can fill up cache with the logging of big history of visited web pages. This app is a light-weight web browser which is both secure and private with no ads and no popups, just the plain Internet shown in full screen. The app does not store all user data, so the navigation of webpages is done in incognito mode. The app was made to open any new HTML5 web page in a secure and private mode with big focus on loading speed of the web pages.

The wide-spread adoption of system defenses such as the randomization of code, stack, and heap raises the bar for code-reuse attacks. Thus, attackers utilize a scripting engine in target programs like a web browser to prepare the code-reuse chain, e.g., relocate gadget addresses or perform a just-in-time gadget search. However, many types of programs do not provide such an execution context that an attacker can use. Recent advances in data-oriented programming (DOP) explored an orthogonal way to abuse memory corruption vulnerabilities and demonstrated that an attacker can achieve Turing-complete computations without modifying code pointers in applications. As of now, constructing DOP exploits requires a lot of manual work-for every combination of application and payload anew. In this paper, we present novel techniques to automate the process of generating DOP exploits. We implemented a compiler called STEROIDS that leverages these techniques and compiles our high-level language SLANG into low-level DOP data structures driving malicious computations at run time. This enables an attacker to specify her intent in an application-and vulnerability-independent manner to maximize reusability. We demonstrate the effectiveness of our techniques and prototype implementation by specifying four programs of varying complexity in SLANG that calculate the Levenshtein distance, traverse a pointer chain to steal a private key, relocate a ROP chain, and perform a JIT-ROP attack. STEROIDS compiles each of those programs to low-level DOP data structures targeted at five different applications including GStreamer, Wireshark and ProFTPd, which have vastly different vulnerabilities and DOP instances. Ultimately, this shows that our compiler is versatile, can be used for both 32-bit and 64-bit applications, works across bug classes, and enables highly expressive attacks without conventional code-injection or code-reuse techniques in applications lacking a scripting engine.

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.

Web attacks have increased rapidly in recent years. However, traditional methods are useless to track web attackers. Browser fingerprint, as a stateless tracking technique, can be used to solve this problem. Given browser fingerprint changes easily and frequently, it is easy to lose track. Therefore, we need to improve the stability of browser fingerprint by linking the new one to the previous chain. In this paper, we propose LSTM model to learn the potential relationship of browser fingerprint evolution. In addition, we adjust the input feature vector to time series and construct training set to train the model. The results show that our model can construct the tracking chain perfectly well with average ownership up to 99.3%.