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User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution. Existing detection approaches generally focus on only one aspect of advertising or tracking (e.g. URL patterns, code structure), making existing approaches susceptible to evasion.In this work we present ADGRAPH, a novel graph-based machine learning approach for detecting advertising and tracking resources on the web. ADGRAPH differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources. Because ADGRAPH considers many aspects of the context a network request takes place in, it is less susceptible to the single-factor evasion techniques that flummox existing approaches.We evaluate ADGRAPH on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists. We implement ADGRAPH as a modification to Chromium. ADGRAPH adds only minor overhead to page loading and execution, and is actually faster than stock Chromium on 42% of websites and AdBlock Plus on 78% of websites. Overall, we conclude that ADGRAPH is both accurate enough and performant enough for online use, breaking comparable or fewer websites than popular filter list based approaches.

Purpose This study aims to focus on delineating the drivers of intention to adopt mobile banking (m-banking) and its actual use among Islamic banking customers by extending the UTAUT2 model with the trust factor. The study also examined the moderating roles of age, gender and experience in the model. Design/methodology/approach An explanatory research design was used, and an online survey was conducted to collect responses from Islamic banking customers. A total of 329 completed responses were used to analyze the data. The partial least squares method was used for data analysis, and a multi-group analysis was applied for moderation-related analysis. Findings Trust positively and significantly influences the behavioral intention to adopt m-banking among Islamic banking customers. In addition, social influence, effort expectancy, hedonic motivation and habits significantly influence behavioral intentions among Islamic banking customers. Originality/value This study provides an extended UTAUT2 model that has never been tested in the context of Islamic m-banking. In addition, this study is expected to be the first scholarly research on Islamic banking in the Maldives.

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Browser fingerprinting is a stateless tracking technique that aims to combine information exposed by multiple different web APIs to create a unique identifier for tracking users across the web. Over the last decade, trackers have abused several existing and newly proposed web APIs to further enhance the browser fingerprint. Existing approaches are limited to detecting a specific fingerprinting technique(s) at a particular point in time. Thus, they are unable to systematically detect novel fingerprinting techniques that abuse different web APIs. In this paper, we propose FP-Radar, a machine learning approach that leverages longitudinal measurements of web API usage on top-100K websites over the last decade for early detection of new and evolving browser fingerprinting techniques. The results show that FP-Radar is able to early detect the abuse of newly introduced properties of already known (e.g., WebGL, Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for browser fingerprinting. To the best of our knowledge, FP-Radar is the first to detect the abuse of the Visibility API for ephemeral fingerprinting in the wild.