MadDroid: Characterising and Detecting Devious Ad Content for Android Apps

Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: \emph{ad loading content} and \emph{ad clicking content}. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6\% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem.Comment: To be published in The Web Conference 2020 (WWW'20

Internet Filters: A Public Policy Report (Second edition; fully revised and updated)

No sooner was the Internet upon us than anxiety arose over the ease of accessing pornography and other controversial content. In response, entrepreneurs soon developed filtering products. By the end of the decade, a new industry had emerged to create and market Internet filters....Yet filters were highly imprecise from the beginning. The sheer size of the Internet meant that identifying potentially offensive content had to be done mechanically, by matching "key" words and phrases; hence, the blocking of Web sites for "Middlesex County," or words such as "magna cum laude". Internet filters are crude and error-prone because they categorize expression without regard to its context, meaning, and value. Yet these sweeping censorship tools are now widely used in companies, homes, schools, and libraries. Internet filters remain a pressing public policy issue to all those concerned about free expression, education, culture, and democracy. This fully revised and updated report surveys tests and studies of Internet filtering products from the mid-1990s through 2006. It provides an essential resource for the ongoing debate

Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content. Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study

Blocking Ad Blockers, 16 J. Marshall Rev. Intell. Prop. L. 272 (2017)

The prevalence of ad blocking software (software that prevents the loading of web based advertisements) is a growing problem for website owners and content creators who rely on advertising revenue to earn money. While the number of ad block users continues to increase, there has thus far been no significant legal challenge to ad blocking in the United States. This comment examines how a website owner, through a combination of technological improvements and the anti-circumvention provisions of the Digital Millennium Copyright Act, could successfully raise a legal challenge against the purveyors of ad blocking software