Parallelized sub-resource loading for web rendering engine

Drive-by-download attacks are client-side attacks that originate from web servers that are visited by web browsers. While many web browsers are vulnerable to the drive-by-download attacks, the cost of detecting malicious web pages that launch drive-by-download attacks is expensive. High-interaction client honeypots are security devices capable of detecting malicious web pages; however, their slow and expensive operations in web page visiting have been considered as a problem. The high-interaction client honeypots employ a visitation algorithm to pinpoint which page has made an unauthorized change of system state when any unauthorized change of the system state occurred after visiting suspicious web pages. To improve the performance of the high-interaction client honeypots, we propose a new visitation algorithm, logarithmic divide-and-conquer (LDAC), for identifying malicious web pages. The LDAC algorithm is an enhanced version of the existing binary divide-and-conquer (BDAC) algorithm. If any system state is abnormally changed after having visited k suspicious web pages concurrently, our LDAC algorithm divides the buffer of k pages into [log2k] pieces and recursively visits the pieces until the malicious page or pages are identified, while the BDAC splits the buffer into k/2 portions. Experimental results show that the LDAC has improved performance of the system up to 15 percent compared to the BDAC algorithm.

Development of web browser prototype with embedded classification capability for mitigating Cross-Site Scripting attacks

WWW has developed rapidly, and it is becoming easy to make personal web sites. In general, we create and edit web pages by using a HTML authoring software or writing HTML source codes in a text editor. Then, we need to upload them to a web server. When we make and build our own web pages by the existing tools, it takes a lot of time and effort to complete the necessary tasks. In this paper, we propose a home page authoring support system in which we directly edit and make web pages on a web browser. Current experimental results demonstrate that our system can effectively support novices to create their web pages. Also, we show two real-world applications that effectively utilize our system.

Non-linear least squares fit of specific heat data within the Schotte–Schotte model using web page

A Novel Approach to HTML Page Creation Using Neural Network

With conventional regression test tools for Web applications, it is difficult to specify the values to be verified that are repeated output inside loop statements. We need to select each of these values displayed in Web pages with the mouse. This paper presents a method that automates regression testing of Web applications on the server side. This method makes it easy to specify values to be verified and enables us to specify them when we develop Web applications. We can directly check the verification results on the Web pages that are shown in the Web browser. If we find a fault, we can even directly modify a part of the server program in the Web browser that corresponds to the currently displayed Web page.

The study of recommendation services based on eye-tracking and social behavior analysis was conducted using either implicit or explicit data, and thus, carried the disadvantage of a decreased recommendation accuracy, having failed to supplement the flaws of each type of data. Therefore, the present study proposes a system applicable to recommendation services after deducting the personal preferences of the user by combining and analyzing the implicit data of eye-tracking and personal social behavior data with the explicit data of purchase data. By conducting experiments capable of obtaining category preferences based on smart phones, tablet PC, and smart TV, the study confirms changing preferences following the characteristics of the smart device. Ultimately, the study attempts to increase the accuracy of recommendations by using both implicit and explicit data and to achieve a recommendation system based on a collaborative filtering that considers device characteristics.

CHAPTER 1 - Cross-Site Scripting

WebWriter: A browser-based editor for constructing Web applications

Web browsers are often used as a popular means for compromising Internet hosts. An attacker may inject a JavaScript malware into a web page. When a victim visits this page, the malware is executed and attempts to exploit a specific browser vulnerability or download an unwanted program. Obfuscated JavaScript malware can easily evade signature-based detection by changing the appearance of JavaScript code. To address this problem, some previous studies have used static analysis in which some features are extracted from both benign and malicious web pages, and then a classifier is trained to distinguish between them. Because nowadays benign JavaScript code is often obfuscated, static analysis techniques generate many false alarms. In this paper, we use dynamic analysis to monitor a web page for detecting obfuscated JavaScript malware. We first load a set of malicious web pages in a real web browser and collect a sequence of predictive function calls using internal function debugging for each of them. We then group similar sequences into the same cluster based on the normalized Levenshtein distance (NLD) metric and generate a so-called behavioral signature for each cluster. A web page is detected as malicious only if the sequence of its intercepted function calls is matched with at least one generated behavioral signature. Our evaluation results show that the generated behavioral signatures are able to detect obfuscated JavaScript malware with a low false alarm rate.

Nowadays, one of the major internet security problems being faced is ‘Web Phishing’, whereby attackers get hold of the personal and sensitive information of the internet users. Sometimes, attackers create fake web pages just to mislead users and give them wrong information. With the increase of more and more sophisticated attacks like Whale Phishing, Spear Phishing, and Ransomware among others, internet users easily fall in attackers’ traps. Most web browsers are not able to counteract or block these attacks and hence internet users consider the spoofed webpages to be legitimate ones and end up giving their details like credit cards details, passwords and usernames among others. In this paper, an application has been developed in Java that performs several tests on a URL, on the different hyperlinks present on the web page and on the content of the web page and provides a security rating to the internet user. Together with the percentage security, the user is informed if the web page is safe, doubtful or unsafe. The security ratings of several website domains such as, .gov, .co, .edu, .info, .mu, .ac, .org, .net, .com were also analysed. Furthermore, tests using independent samples ANOVA and Tukey HSD were performed and they revealed that there was a significant difference between the security ratings of the websites.

Much has been written concerning the difficulties faced by visually handicapped persons when they access the internet. To solve some of the problems and to make web pages more accessible, we developed a tool we call the "Easy Bar," which works as a toolbar on the web browser. The functions of the Easy Bar are to change the size of web texts and images, to adjust the color, and to clear cached data that is automatically saved by the web browser. These functions are executed with ease by clicking buttons and operating a pull-down list. Since the icons built into Easy Bar are quite large, it is not necessary for the user to deal with delicate operations. The functions of Easy Bar run on any web page without increasing the processing time. For the visually handicapped, Easy Bar would contribute greatly to improved web accessibility to medical information.

We present a way to run Objective Caml programs on a standard, unmodified web browser, with a compatible data representation and execution model, including concurrency. To achieve this, we designed a bytecode interpreter in JavaScript, as well as an implementation of the runtime library. Since the web browser does not provide the same interaction mechanisms as a typical Objective Caml environment, we provide an add-on to the standard library, enabling interaction with the web page. As a result, one can now build the client side of a web application with the standard Objective Caml compiler and run it on any modern web browser.

Portable web browser is a stand-alone web browser, which is designed to run on web pages and applications on an operating system independently. Portable web browsers store artifacts in the installed in the folder, while normal web browsers store artifacts in the user’s system. Therefore, portable web browsers are difficult to judge whether that users used portable web browsers. This paper describes whether that manufacturer support portable web browser and find out about the artifact path of portable web browsers. Then, we propose analysis framework to detect artifacts of portable web browsers through ‘UserAssist’ key value and prefetch file and explain the each module of framework.

Web browsers have become an integral part of our daily lives, granting us access to vast information and services. However, this convenience significantly risks personal information and data security. One common source of this risk is browser extensions, which users often employ to add new features to their browsers. Unfortunately, these extensions can also pose a security threat, as malicious ones may access and steal sensitive information such as passwords, credit card details, and personal data. The vulnerability of web browsers to malicious extensions is a significant challenge that effectively tackles through robust defence mechanisms. To address this concern, Secure Vault – API is proposed and designed to safeguard confidential web page content from malicious extensions. The Web Crypto API provides cryptographic functions that protect data during transmission and storage. The Secure Vault encompasses a Sentinel extension responsible for parsing the web page’s Document Object Model (DOM) content and querying for all “vault” elements. The extension then verifies that the DOM content has not been tampered with by any malicious extension by calculating the SHA512 hash value of the concatenated vault elements as a string, with no whitespace between them. With its encryption, hashing, and isolation techniques, the Secure Vault effectively protects confidential web page content from malicious extensions. It provides a secure environment for storing and processing sensitive data, reducing the risk of data breaches caused by malicious extensions. The proposed approach offers significant advantages over existing strategies in terms of protecting confidential web page content from malicious extensions. This not only improves the efficiency and effectiveness of the browser extensions but also ensures compatibility, interoperability and performance across different web browsers with respect to the load time of HTML elements. Users can browse the web and carry out sensitive transactions with peace of mind, knowing their data is safeguarded against theft or manipulation by malicious extensions.