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Although the cryptocurrency hype over the past year may be seen by some as a benign social fad, to the Web community it is the center point for a series of ethically dubious ransomware attacks. Browser based cryptomining, or cryptojacking has gained widespread attention. Cryptojacking consists of Web servers delivering cryptocurrency mining scripts to clients, and using the client resources to play part in a distributed coin mining scheme. Although Web server operators defend the ethics of their involvement by quoting mining as a substitute for advertisement revenue, these scripts can hog massive amounts of client-side resources and can be delivered without client consent, presenting a high potential for abuse. Regardless of how ethical these campaigns are, what remains constant is the need for their detection. While there has been an array of work in defending against such cryptojacking campaigns, these defenses remain quite preliminary. We present CoinSpy, an entirely in-browser tool built using deep learning techniques for the detection of cryptomining activity within Web pages. A key challenge is that there is limited visibility into the client resource usage from within the browser sandbox. CoinSpy extracts several signals from information available from the browser and combines them using deep learning to build a powerful cryptojacking classifier. We argue why CoinSpy is the most robust defense against current and future cryptojacking attacks as compared to recent work, and show that it can detect various cryptojacking campaigns with 97% accuracy.

Page Load Time (PLT) is critical in measuring web page load performance. However, the existing PLT metrics are designed to measure the Web page load performance on desktops/laptops and do not consider user interactions on mobile browsers. As a result, they are ill-suited to measure mobile page load performance from the perspective of the user. In this work, we present the Mobile User-Centered Page Load Time Estimator (muPLT est), a model that estimates the PLT of users on Web pages for mobile browsers. We show that traditional methods to measure user PLT for desktops are unsuited to mobiles because they only consider the initial viewport, which is the part of the screen that is in the user's view when they first begin to load the page. However, mobile users view multiple viewports during the page load process since they start to scroll even before the page is loaded. We thus construct the muPLT est to account for page load activities across viewports. We train our model with crowdsourced scrolling behavior from live users. We show that muPLT est predicts ground truth user-centered PLT, or the muPLT, obtained from live users with an error of 10-15% across 50 Web pages. Comparatively, traditional PLT metrics perform within 44-90% of the muPLT. Finally, we show how developers can use the muPLT est to scalably estimate changes in user experience when applying different Web optimizations. CCS CONCEPTS • Human-centered computing → User models.

For many years, the research community, practitioners, and regulators have used myriad methods and tools to understand the complex structure and behavior of ISPs from the edge of the network. Unfortunately, the nature of these techniques forces the researcher to find a balance between ISP-coverage, user scale, and accuracy. In this paper we present AdTag, a network measurement paradigm that leverages the opportunistic nature of online targeted advertising to measure the Internet from the edge of the network. We discuss and formalize AdTag's design space-including technical, ethical, deployability and economic factors-and its potential to analyze a wide spectrum of Internet connectivity aspects from the browser. We run several experiments to demonstrate that AdTag can be tailored towards geographic and devicebased user groups, finding also several challenges to be faced in order to maximize the number of samples. In a 7-day campaign, AdTag could access more than 20K ISPs at a global scale (185 countries) using millions of edge nodes.