Automated Testing of Your Corporate Website from Multiple Countries with Selenium Contents
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Chrome Devtools Protocol (CDP)
e e c r i è t t s s u i n J i a M l e d Headless Chr me Automation with THE CRRRI PACKAGE Romain Lesur Deputy Head of the Statistical Service Retrouvez-nous sur justice.gouv.fr Web browser A web browser is like a shadow puppet theater Suyash Dwivedi CC BY-SA 4.0 via Wikimedia Commons Ministère crrri package — Headless Automation with p. 2 de la Justice Behind the scenes The puppet masters Mr.Niwat Tantayanusorn, Ph.D. CC BY-SA 4.0 via Wikimedia Commons Ministère crrri package — Headless Automation with p. 3 de la Justice What is a headless browser? Turn off the light: no visual interface Be the stage director… in the dark! Kent Wang from London, United Kingdom CC BY-SA 2.0 via Wikimedia Commons Ministère crrri package — Headless Automation with p. 4 de la Justice Some use cases Responsible web scraping (with JavaScript generated content) Webpages screenshots PDF generation Testing websites (or Shiny apps) Ministère crrri package — Headless Automation with p. 5 de la Justice Related packages {RSelenium} client for Selenium WebDriver, requires a Selenium server Headless browser is an old (Java). topic {webshot}, {webdriver} relies on the abandoned PhantomJS library. {hrbrmstr/htmlunit} uses the HtmlUnit Java library. {hrbrmstr/splashr} uses the Splash python library. {hrbrmstr/decapitated} uses headless Chrome command-line instructions or the Node.js gepetto module (built-on top of the puppeteer Node.js module) Ministère crrri package — Headless Automation with p. 6 de la Justice Headless Chr me Basic tasks can be executed using command-line -
Automated Testing Clinic Follow-Up: Capybara-Webkit Vs. Poltergeist/Phantomjs | Engineering in Focus
Automated Testing Clinic follow-up: capybara-webkit vs. polter... https://behindthefandoor.wordpress.com/2014/03/02/automated-... Engineering in Focus the Fandor engineering blog Automated Testing Clinic follow-up: capybara-webkit vs. poltergeist/PhantomJS with 2 comments In my presentation at the February Automated Testing SF meetup I (Dave Schweisguth) noted some problems with Fandor’s testing setup and that we were working to fix them. Here’s an update on our progress. The root cause of several of our problems was that some of the almost 100 @javascript scenarios in our Cucumber test suite weren’t running reliably. They failed occasionally regardless of environment, they failed more on slower CPUs (e.g. MacBook Pros only a couple of years old), when they failed they sometimes hung forever, and when we killed them they left behind webkit-server processes (we were using the capybara-webkit driver) which, if not cleaned up, would poison subsequent runs. Although we’ve gotten pretty good at fixing flaky Cucumber scenarios, we’d been stumped on this little handful. We gave up, tagged them @non_ci and excluded them from our build. But they were important scenarios, so we had to run them manually before deploying. (We weren’t going to just not run them: some of those scenarios tested our subscription process, and we would be fools to deploy a build that for all we knew wouldn’t allow new users to subscribe to Fandor!) That made our release process slower and more error-prone. It occurred to me that I could patch the patch and change our deployment process to require that the @non_ci scenarios had been run (by adding a git tag when those scenarios were run and checking for it when deploying), but before I could put that in to play a new problem appeared. -
Client-Side Diversification for Defending Against
Everyone is Different: Client-side Diversification for Defending Against Extension Fingerprinting Erik Trickel, Arizona State University; Oleksii Starov, Stony Brook University; Alexandros Kapravelos, North Carolina State University; Nick Nikiforakis, Stony Brook University; Adam Doupé, Arizona State University https://www.usenix.org/conference/usenixsecurity19/presentation/trickel This paper is included in the Proceedings of the 28th USENIX Security Symposium. August 14–16, 2019 • Santa Clara, CA, USA 978-1-939133-06-9 Open access to the Proceedings of the 28th USENIX Security Symposium is sponsored by USENIX. Everyone is Different: Client-side Diversification for Defending Against Extension Fingerprinting Erik Trickel?, Oleksii Starov†, Alexandros Kapravelos‡, Nick Nikiforakis†, and Adam Doupé? ?Arizona State University †Stony Brook University {etrickel, doupe}@asu.edu {ostarov, nick}@cs.stonybrook.edu ‡North Carolina State University [email protected] Abstract by users, as they see fit, by installing browser extensions. Namely, Google Chrome and Mozilla Firefox, the browsers Browser fingerprinting refers to the extraction of attributes with the largest market share, offer dedicated browser exten- from a user’s browser which can be combined into a near- sion stores that house tens of thousands of extensions. In turn, unique fingerprint. These fingerprints can be used to re- these extensions advertise a wide range of additional features, identify users without requiring the use of cookies or other such as enabling the browser to store passwords with online stateful identifiers. Browser extensions enhance the client- password managers, blocking ads, and saving articles for later side browser experience; however, prior work has shown that reading. their website modifications are fingerprintable and can be From a security perspective, the ability to load third-party used to infer sensitive information about users. -
Interstitial Content Detection Arxiv:1708.04879V1 [Cs.CY] 13 Aug
Interstitial Content Detection Elizabeth Lucas, Mozilla Research August 2017 Abstract Interstitial content is online content which grays out, or otherwise obscures the main page content. In this technical report, we discuss exploratory research into detecting the presence of interstitial content in web pages. We discuss the use of computer vision techniques to detect interstitials, and the potential use of these techniques to provide a labelled dataset for machine learning. 1. Introduction The structure and underlying nature of content in the web is fundamentally different than most rigorously structured data, and often requires deviating from the traditional approaches of recognizing patterns in more heavily structured data. Within the types of content on the web, interstitials are of interest due to their interrupting of the user's web experience. This report represents the preliminary research necessary to explore the structure of interstitial content, and the beginnings of a machine learning application to assist with our understanding of web content and interstitials. The scripts used for data collection and evaluation are available [1]. 1.1. Definitions For the purpose of this research project, `interstitials', or `interstitial content', are defined as online content, often advertisements or other promotional content, which grays out or otherwise obscures the main page content. These interstitials often require the user to interact in order to return to the main content, interrupting the user's experience. `Servo' refers to the Servo browser engine, sponsored by Mozilla Research [6]. Written in the Rust programming language, this modern parallel browser engine aims to improve performance, security, modularity, and parallelization. Future work will involve eventually bringing interstitial ad detection into the Servo browser engine itself. -
Casperjs Documentation Release 1.1.0-DEV
CasperJs Documentation Release 1.1.0-DEV Nicolas Perriault Sep 13, 2018 Contents 1 Installation 3 1.1 Prerequisites...............................................3 1.2 Installing from Homebrew (OSX)....................................4 1.3 Installing from npm...........................................4 1.4 Installing from git............................................4 1.5 Installing from an archive........................................5 1.6 CasperJS on Windows..........................................5 1.7 Known Bugs & Limitations.......................................6 2 Quickstart 7 2.1 A minimal scraping script........................................7 2.2 Now let’s scrape Google!........................................8 2.3 CoffeeScript version...........................................9 2.4 A minimal testing script......................................... 10 3 Using the command line 11 3.1 casperjs native options.......................................... 12 3.2 Raw parameter values.......................................... 13 4 Selectors 15 4.1 CSS3................................................... 15 4.2 XPath................................................... 16 5 Testing 17 5.1 Unit testing................................................ 17 5.2 Browser tests............................................... 18 5.3 Setting Casper options in the test environment............................. 19 5.4 Advanced techniques........................................... 20 5.5 Test command args and options.................................... -
API Measurement
Serving Two Masters An Empirical Study of Browser API Cooptation Pete Snyder, Chris Kanich University of Illinois at Chicago Less More Features Features Less More Features Features Managed Pointer Memory Arithmetic Outline • Browser Complexity is Increasing • Complexity is Often Not Useful • Complexity is Harmful to Privacy • Is Complexity is Harmful to Security? 1. Browser Complexity is Growing 1993: Mosaic 1995: Netscape 2.0 1996: CSS 1998: DOM1 1999: AJAX / XMLHttpRequest Observations • API growth started off very slow • API growth was “document” centric • “Broad” APIs API Growth 2013 2014 2015 • CSSOM View Module • Calendar API • Encrypted Media Extensions • Web Audio API • Messaging API • Web MIDI • Proximity Events • RDF Extensions • Service Workers • Crypto Extensions • Progress events • Performance API • • Touch Events Network Info API • Raw Socket API • GeoLocation API • Ambient Light API • WebDriver API • Pointer API • HTML 5 • SVG 2 API • CSS Animations • WebCrypto API • WebRTC 2. Is This Complexity Useful? Determining API “Usefulness” • Measure how often APIs are called • Decide whether those calls are “useful" • Simulate real world web browsing Measuring API Calls • Selected 45 APIs and features • Instrumented PhantomJS / WebKit • Implemented missing APIs “Usefulness” Oracle • Subjective measure • Ghostery and AdBlock+ filter rules • Measure API usage pre-and-post filters Simulated Browsing • Alexa 10,000 • 10,000 random URLs • 10,000 random Hosts • “Random” sites taken from searching UNIX dictionary tri-grams on DDG AJAX DOM 1 + 2 APIs Rare APIs API Name URLs Battery API 21 Page Transition API 9 GeoLocation API 55 Shadow DOM 5 Non-used APIs • IndexDB • SVG API • WebGL • Vibration API • WebRTC • WebAudio API • Browser Name API • WebWorker API • Gamepad API GeoLocation API Touch Events API 3. -
Python Selenium Download Pdf Chrome Driver How to Download Dynamically Loaded Content Using Python
python selenium download pdf chrome driver How to download dynamically loaded content using python. When you surf online, you occasionally visit websites that show content like videos or audio files which are dynamically loaded. This is basically done using AJAX calls or sessions where the URLs for these files are generated in some way for which you can not save them by normal means. A scenario would be like, for example, you visited a web page and a video is loaded through a video player like jwplayer after 1 or 2 secs. You want to save this but unfortunately you couldn’t do so by right-clicking on it and saving it as the player doesn’t show that option. Even if you use command line tool like wget or youtube-dl, it might be possible but for some reason it just doesn’t work. Another problem is that, there are still some javascript functions that need to be executed and until then, the video url does not get generated in the site. Now the question is, how would you download such files to your computer? Well, there are different ways to download them. One way is to use a plugin or extension for the browser like Grab Any Media or FlashGot which can handle such requests and might allow you to download it. Now the thing is, lets say you want to download an entire set of videos that are loaded using different AJAX calls. In this case, the plugins or extensions, might work but it takes a long time to manually download each file. -
Angular Vs React.Js Vs Vue.Js
EVERYTHING YOU NEED TO KNOW TO MAKE AN EDUCATED DECISION ABOUT YOUR TECHNOLOGY STACK ANGULAR VS REACT.JS VS VUE.JS + Our evaluation of UI Frameworks, Tools & Technologies 2019 3 What’s inside? 1 Preface 3 2 Angular, ReactJS & Vue JS - Comparison 4 3 Angular, ReactJS & Vue JS - Pros & Cons 6 4 Angular, ReactJS & Vue JS - Conclusion 11 5 UI Technoverse 13 a.Frameworks 15 b.Technologies 19 c.Tools 22 2 Are you a front-end superstar, excited to build feature-rich beautiful UI? Join Us 3 STATE OF FRONTEND TECHSTACKS 2019 PREFACE Preface he only constant in the web frontend development landscape is that it is in constant flux year after year. So, it becomes paramount to reevaluate the tools, frameworks, Tlibraries and practices after every few quarters. As of this writing most of the web frontend development happens in either Angular, ReactJS or VueJS. Compared to our previous evaluation in 2018 which only had Angular and ReactJS as major players, now, we also have VueJS with significant traction. As always a direct comparison between Angular, ReactJS and VueJS alone will not be sufficient. So, it will not be an individual comparison but a comparison of their respective ecosystems on the whole. 3 Are you a front-end superstar, excited to build feature-rich beautiful UI? Join Us Angular, React.JS & Vue.JS - Comparison In this section, we compare all three frameworks using a plethora of parameters to highlight how they fare against 2each other. STATE OF FRONTEND TECHSTACKS 2019 COMPARISON Angular ReactJS VueJS Type JavaScript Framework JavaScript Library JavaScript Library Web development and Web development and Web development and Hybrid mobile app Native mobile app Hybrid mobile app Used for development (Ionic) development (React development Native) (Onsen UI) Maintained by Google & Community Facebook & Community Community TypeScript Javascript (Also Javascript (Also Coded in supports Typescript) supports Typescript) Steep learning curve Easier Easiest among the three Ease of Learning since it is an end to end framework. -
Creating the World's Largest Real-Time Camera Network
Creating the World’s Largest Real-Time Camera Network Ryan Dailey, Ahmed S. Kaseb, Chandler Brown, Sam Jenkins, Sam Yellin, Fengjian Pan, Yung-Hsiang Lu; Purdue University; West Lafayette, Indiana, USA Abstract so on. This contextual information is called metadata and can be Millions of cameras are openly connected to the Internet for helpful for data analytics. Metadata can be useful for identify- a variety of purposes. This paper takes advantage of this resource ing the cameras for specific purposes, for example, traffic cam- to gather visual data. This camera data could be used for a myr- eras for studying urban transportation. The second problem is iad of purposes by solving two problems. (i) The network camera the wide range of protocols used to retrieve data from network image data needs context to solve real world problems. (ii) While cameras. Different brands of network cameras need different re- contextual data is available, it is not centrally aggregated. The trieval methods (for example, different paths for HTTP GET com- goal is to make it easy to leverage the vast amount of network mands). Many organizations (such as departments of transporta- cameras. tion in different cities) aggregate the data streams from multiple The database allows users to aggregate camera data from cameras. There is no standard as to how the information is ag- over 119,000 network camera sources all across the globe in real gregated and thus, there is no standard method for retrieving data time. This paper explains how to collect publicly available infor- from different sources. mation from network cameras. -
System for Detection of Websites with Phishing and Other Malicious Content
Masaryk University Faculty of Informatics System for detection of websites with phishing and other malicious content BachelorŠs Thesis Tomáš Ševčovič Brno, Fall 2017 Declaration Hereby I declare that this paper is my original authorial work, which I have worked out on my own. All sources, references, and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source. Tomáš Ševčovič Advisor: prof. RNDr. Václav Matyáš, M.Sc., Ph.D. i Acknowledgement I would like to thank prof. RNDr. Václav Matyáš, M.Sc., Ph.D. for the management of the bachelor thesis, valuable advice and comments. I would also like to thank the consultant from CYAN Research & Development s.r.o., Ing. Dominik Malčík, for useful advice, dedicated time and patience in consultations and application development. Also, I would like to thank my family and friends for their support throughout my studies and work on this thesis. ii Abstract The main goal of this bachelor thesis is to create a system for detection of websites with phishing and other malicious content with respect to Javascript interpretation. The program should be able to download and process thousands of domains and get positive results. The Ąrst step involves examining an overview of automated web testing tools to Ąnd an optimal tool which will be used in the main implementation. The thesis contains overview of technologies for website testing, their comparison, overview of malware methods on websites, implementation and evaluation of the system. iii Keywords Chrome, Javascript, link manipulation, malware, phishing, URL redi- rects, XSS, Yara iv Contents 1 Introduction 1 2 Overview of approaches to website testing 3 2.1 Manual testing ....................... -
Flare-On 3: Challenge 10 Solution - FLAVA
Flare-On 3: Challenge 10 Solution - FLAVA Challenge Authors: FireEye Labs Advanced Vulnerability Analysis Team (FLAVA) Intro The first part of FLAVA challenge is a JavaScript puzzle based on the notorious Angler Exploit Kit (EK) attacks in 2015 and early 2016. All classic techniques leveraged by Angler, along with some other intriguing tricks utilized by certain APT threat actors, are bundled into this challenge. While Angler EK’s activities have quieted down since the takedown of a Russian group “Lurk” in mid 2016, its advanced anti-analysis and anti-detection techniques are still nuggets of wisdom that we think are worth sharing with the community for future reference. Walkthrough Compromising or malvertising on legitimate websites are the most common ways that exploit kits redirect victims to their landing pages. Exploit kits can redirect traffic multiple times before serving the victim the landing page. Once on the landing page, the exploit kit profiles the victim’s environment, and may choose to serve an exploit that affects it. In the case of this challenge, the landing page is only one redirect away from the compromised site (hxxp://10.11.106.81: 18089/flareon_found_in_milpitas.html). Often, Angler’s (and many other EKs’) landing page request has a peculiar, and thus fishy, URL after some seemingly legit HTTP transactions. Layer-0 Walkthrough The landing page may initially seem daunting, and that’s exactly the purpose of obfuscation. But it’s still code, regardless of the forms it may take. After all, it’s supposed to carry out the attack without any user interaction. It’s strongly advised to rewrite the code with meaningful names during analysis, just as one would in IDA when analyzing binaries. -
Identifying Javascript Skimmers on High-Value Websites
Imperial College of Science, Technology and Medicine Department of Computing CO401 - Individual Project MEng Identifying JavaScript Skimmers on High-Value Websites Author: Supervisor: Thomas Bower Dr. Sergio Maffeis Second marker: Dr. Soteris Demetriou June 17, 2019 Identifying JavaScript Skimmers on High-Value Websites Thomas Bower Abstract JavaScript Skimmers are a new type of malware which operate by adding a small piece of code onto a legitimate website in order to exfiltrate private information such as credit card numbers to an attackers server, while also submitting the details to the legitimate site. They are impossible to detect just by looking at the web page since they operate entirely in the background of the normal page operation and display no obvious indicators to their presence. Skimmers entered the public eye in 2018 after a series of high-profile attacks on major retailers including British Airways, Newegg, and Ticketmaster, claiming the credit card details of hundreds of thousands of victims between them. To date, there has been little-to-no work towards preventing websites becoming infected with skimmers, and even less so for protecting consumers. In this document, we propose a novel and effective solution for protecting users from skimming attacks by blocking attempts to contact an attackers server with sensitive information, in the form of a Google Chrome web extension. Our extension takes a two-pronged approach, analysing both the dynamic behaviour of the script such as outgoing requests, as well as static analysis by way of a number of heuristic techniques on scripts loaded onto the page which may be indicative of a skimmer.