Today, more than 3 million websites rely on online advertising revenue. Despite the monetary incentives, ads often frustrate users by disrupting their experience, interrupting content, and slowing browsing. To improve ad experiences, leading media associations define Better Ads Standards for ads that are below user expectations. However, little is known about how well websites comply with these standards and whether existing approaches are sufficient for developers to quickly resolve such issues. In this paper, we propose AdHere, a technique that can detect intrusive ads that do not comply with Better Ads Standards and suggest repair proposals. AdHere works by first parsing the initial web page to a DOM tree to search for potential static ads, and then using mutation observers to monitor and detect intrusive (dynamic/static) ads on the fly. To handle ads’ volatile nature, AdHere includes two detection algorithms for desktop and mobile ads to identify different ad violations during three phases of page load events. It recursively applies the detection algorithms to resolve nested layers of DOM elements inserted by ad delegations. We evaluate AdHere on Alexa Top 1 Million Websites. The results show that AdHere is effective in detecting violating ads and suggesting repair proposals. Comparing to the current available alternative, AdHere detected intrusive ads on 4,656 more mobile websites and 3,911 more desktop websites, and improved recall by 16.6% and accuracy by 4.2%.
Wed 17 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Software security and privacyTechnical Track / Journal-First Papers at Meeting Room 103 Chair(s): Wei Yang University of Texas at Dallas | ||
13:45 15mTalk | BFTDetector: Automatic Detection of Business Flow Tampering for Digital Content Service Technical Track I Luk Kim Purdue University, Weihang Wang University of Southern California, Yonghwi Kwon University of Virginia, Xiangyu Zhang Purdue University | ||
14:00 15mTalk | FedSlice: Protecting Federated Learning Models from Malicious Participants with Model Slicing Technical Track Ziqi Zhang Peking University, Yuanchun Li Institute for AI Industry Research (AIR), Tsinghua University, Bingyan Liu Peking University, Yifeng Cai Peking University, Ding Li Peking University, Yao Guo Peking University, Xiangqun Chen Peking University | ||
14:15 15mTalk | PTPDroid: Detecting Violated User Privacy Disclosures to Third-Parties of Android Apps Technical Track Zeya Tan Nanjing University of Science and Technology, Wei Song Nanjing University of Science and Technology Pre-print | ||
14:30 15mTalk | AdHere: Automated Detection and Repair of Intrusive Ads Technical Track Yutian Yan University of Southern California, Yunhui Zheng , Xinyue Liu University at Buffalo, SUNY, Nenad Medvidović University of Southern California, Weihang Wang University of Southern California | ||
14:45 15mTalk | Bad Snakes: Understanding and Improving Python Package Index Malware Scanning Technical Track | ||
15:00 7mTalk | DAISY: Dynamic-Analysis-Induced Source Discovery for Sensitive Data Journal-First Papers Xueling Zhang Rochester Institute of Technology, John Heaps University of Texas at San Antonio, Rocky Slavin The University of Texas at San Antonio, Jianwei Niu University of Texas at San Antonio, Travis Breaux Carnegie Mellon University, Xiaoyin Wang University of Texas at San Antonio | ||
15:07 7mTalk | Assessing the opportunity of combining state-of-the-art Android malware detectors Journal-First Papers Nadia Daoudi SnT, University of Luxembourg, Kevin Allix CentraleSupelec Rennes, Tegawendé F. Bissyandé SnT, University of Luxembourg, Jacques Klein University of Luxembourg | ||