O'browser

Despite the promise of Web of Things, the synergies between edge computing and the Web have remained largely unexplored. In this demonstration we present Spaceify, an edge computing platform for Web of Things. Spaceify features a web-based application programming model that grants web pages, originating from the Internet, controlled access to local resources. Local resources (Things) are represented by driver applications running on the edge node. The controlled access to local resources is implemented using spacelets, small edge applications that web pages can execute on the edge node on-demand. The spacelets can be selectively granted rights to call the APIs of the edge applications representing the local resources. Any mobile device connected to the local network with a modern web browser can utilize the resources in the space without the need of installing specific mobile applications, which makes Spaceify ideal for public spaces. In this demo, we demonstrate the Spaceify programming model with two use cases we have implemented that enable web pages originating from the Internet to interact with smart lights and big-screens in the space. Audience is invited to interact with the smart lights and the big-screen using the web browsers of their own mobile devices.

A web browser is an application that comes bundled with every consumer operating system, including both desktop and mobile platforms. A modern web browser is complex software that has access to system-level features, includes various plugins and requires the availability of an Internet connection. Like any multifaceted software products, web browsers are prone to numerous vulnerabilities. Exploitation of these vulnerabilities can result in destructive consequences ranging from identity theft to network infrastructure damage. BeEF, the Browser Exploitation Framework, allows taking advantage of these vulnerabilities to launch a diverse range of readily available attacks from within the browser context. Existing defensive approaches aimed at hardening network perimeters and detecting common threats based on traffic analysis have not been found successful in the context of BeEF detection. This paper presents a proof-of-concept approach to BeEF detection in its own operating environment – the web browser – based on global context monitoring, abstract syntax tree fingerprinting and real-time network traffic analysis.

Summary Wellbore visualizations are often depicted by use of 2D perspective views. This approach is limited and potentially misleading. Of particular interest to rod-pumping and progressing-cavity pumps is the amount of side loading placed on the rod string throughout the wellbore. By illustrating the trajectory of a wellbore in 2D, some nuanced geometries can be easily lost. For example, a “corkscrew” wellbore, or any other complex wellbore geometry, can be very difficult to depict through simple perspective views. A corkscrew is best visualized at a specific camera angle where the shape is apparent. This best viewing angle is generally not at the simple 90° angle where typical 2D perspective views are generated. Distributed wellbore-visualization tools can be applied to both the design and analysis of pump installations. Interactive and shared views help to identify points in the well where problems may occur, or have occurred. Strategic placement of downhole equipment such as pump depth relative to perforations and historical fluid levels can be performed in a more intuitive way, taking into account a more accurate understanding of the geometry in a given well. The use of standards-based modern Web technologies enables the rapid deployment of such a tool to both desktop and mobile devices. The 3D graphics acceleration was previously only available in the Web browser through plugins and other third-party software installations. The 3D graphics can now be rendered natively in most modern Web browsers without the need for any additional software installation. Examples of such native visualizations can be found in Fig. 1.

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

Modern web pages are diverse and complex. There is also a wide range of devices, operating systems, and browsers that users use to access these web pages. In this work, we study how web pages, and the traffic generated by their download, differ across these different client types. We conduct a preliminary study that performs a client-side analysis of the network traffic. We identify both expected and unexpected differences among similar web pages across different browser platforms that can be used to drive future internet measurement research and identify potential design decisions and/or bugs in modern browsers.

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.

Several solutions to describing service choreography have emerged, mainly focused on encoding capabilities of services especially for those deployed on the Web. These solutions are either derived from traditional Web service standards such as WSDL or inspired by the theory of process calculus. Little attention has however been paid to finding a lightweight solution which can enable peers to obtain, publish and share service choreography in an open environment or peer-to-peer network. This paper proposes a framework for choreographing semantically enhanced Web Services encoded in a extended lightweight coordinative language which is derived from process calculus and is dedicated to running in modern Web browsers. A proof-of-concept prototype has been implemented and demoed as a decentralised service choreography-management platform based on this framework. There is no need for users to install any third-party application, and service choreography execution is achieved via client-side Web browsers. Also, the preliminary experiments indicate the efficiency and scalability of our proof-of-concept implementation of this framework.

The browser allows users to view and interact with content on the web pages. An attack on the browser provides an unauthorized access, damage or disruption of the user information within or outside the browser. An attacker can compromise Browser by exploiting structural and application level vulnerabilities within Browser. This tutorial discuss the major security issues in modern web browsers, and encompasses the solution directives to address these issues and challenges caused by Browser-based attacks.

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.

Parallelized sub-resource loading for web rendering engine

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

HTML is a standard file format for pages that can be viewed in a Web browser. There are a great many ways to create HTML pages, ranging from graphical editors such as Dreamweaver to text editors such as Notepad, Emacs, or Vim. Unfortunately, graphical editors generally do not work well with most Web frameworks, including Rails, and HTML produced with text editors may not be standards-compliant; it may lack closing tags or use invalid tag combinations or attributes. Additionally, some graphical editors produce bad HTML; the problem is exacerbated by the fact that modern browsers are very tolerant of HTML that is not standardscompliant, so very incorrect HTML will often still display properly.KeywordsText EditorGraphical EditorFormat OptionModern BrowserStandard File FormatThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Web browser history detection using CSS visited styles has long been dismissed as an issue of marginal impact. However, due to recent changes in Web usage patterns, coupled with browser performance improvements, the long-standing issue has now become a significant threat to the privacy of Internet users. In this paper we analyze the impact of CSS-based history detection and demonstrate the feasibility of conducting practical attacks with minimal resources. We analyze Web browser behavior and detectability of content loaded via standard protocols and with various HTTP response codes. We develop an algorithm for efficient examination of large link sets and evaluate its performance in modern browsers. Compared to existing methods our approach is up to 6 times faster, and is able to detect up to 30,000 visited links per second. We present a novel Web application capable of effectively detecting clients' browsing histories and discuss real-world results obtained from 271,576 Internet users. Our results indicate that at least 76% of Internet users are vulnerable to history detection, including over 94% of Google Chrome users; for a test of most popular Internet websites we were able to detect, on average, 62.6 (median 22) visited locations per client. We also demonstrate the potential to profile users based on social news stories they visited, and to detect private data such as zipcodes or search queries typed into online forms.