Qifan Yang, Zhenhua Li (Tsinghua University); Tianyin Xu (UIUC); Ennan Zhai (Alibaba Group Inc.); Yunhao Liu (Tsinghua University); Yuanchao Huang, Jiaming He, Hai Long (Tencent Security)
Playing Android games on Windows x86 PCs has gained enormous popularity in recent years, and the de facto solution is to use mobile emulators built with the AOVB (Android-x86 On VirtualBox) architecture. When playing heavy 3D Android games with AOVB, however, users often suffer unsatisfactory smoothness due to the considerable overhead of full virtualization. This paper presents DAOW, a game-oriented Android emulator implementing the idea of direct Android emulation, which eliminates the overhead of full virtualization by directly executing Android app binaries on top of x86-based Windows. Based on pragmatic, efficient instruction rewriting and syscall emulation, DAOW offers foreign Android binaries direct access to the domestic PC hardware through Windows kernel interfaces, achieving nearly native hardware performance. Moreover, it leverages graphics and security techniques to enhance user experiences and prevent cheating in gaming. As of late 2018, DAOW has been adopted by over 50 million PC users to run thousands of heavy 3D Android games. Compared with AOVB, DAOW improves the smoothness by 21% on average, decreases the game startup time by 48%, and reduces the memory usage by 22%.
Zhiyong Shan (Wichita State University); Raina Samuel, Iulian Neamtiu (New Jersey Institute of Technology)
Device Administrator (DA) capabilities for mobile devices, e.g., remote locking/wiping, or enforcing password strength, were originally introduced to help organizations manage phone fleets or enable parental control. However, DA capabilities have been subverted and abused: malicious apps have used DA to create ransomware or lock users out, while benign apps have used DA to prevent or hinder uninstallation; in certain cases the only remedy is to factory-reset the phone. We call these apps "Deathless Device Administrator" (DDA), i.e., apps that cannot be uninstalled. We provide the first systematic study of Android DA capabilities, DDA apps, DDA-attack resistance across Android versions, and DDA-induced families in malicious apps. To enable scalable studies of questionable DA behavior, we developed DAAX, a static analyzer which exposes potential DA abuse effectively and efficiently. In a corpus of 39,459 apps (20,467 malicious and 18,992 benign) DAAX has found 4,135 DA apps and 691 potential DDA apps. The static analysis results on the 4,135 apps were cross-checked via dynamic analysis on at least 3 phones, confirming 578 true DDAs, including apps currently on Google Play. The study has shown that DAAX is effective (84.8% F-measure) and efficient (analysis typically takes 205 seconds per app).
Byungjin Jun, Fabián E. Bustamante, Sung Yoon Whang (Northwestern University); Zachary S. Bischof (IIJ Research)
The rapid growth in the number of mobile devices, subscriptions and their associated traffic, has served as motivation for several projects focused on improving mobile users' quality of experience (QoE). Few have been as contentious as the Google-initiated Accelerated Mobile Project (AMP), both praised for its seemingly instant mobile web experience and criticized based on concerns about the enforcement of its formats. This paper presents the first characterization of AMP's impact on users' QoE. We do this using a corpus of over 2,100 AMP webpages, and their corresponding non-AMP counterparts, based on trendy-keyword-based searches. We characterized AMP's impact looking at common web QoE metrics, including Page Load Time, Time to First Byte and SpeedIndex (SI). Our results show that AMP significantly improves SI, yielding on average a 60% lower SI than non-AMP pages without accounting for prefetching. Prefetching of AMP pages pushes this advantage even further, with prefetched pages loading over 2,000ms faster than non-prefetched AMP pages. This clear boost may come, however, at a non-negligible cost for users with limited data plans as it incurs an average of over 1.4 MB of additional data downloaded, unbeknownst to users.
Yonghun Choi, Seonghoon Park, Hojung Cha (Yonsei University)
Web browsing, previously optimized for the desktop environment, is being fine-tuned for energy-efficient use on mobile devices. Although active attempts have been made to reduce energy consumption, the advent of energy-aware scheduling (EAS) integrated in the recent devices suggests the possibility of a new approach for optimizing energy use by browsers. Our preliminary analysis showed that the existing EAS-enabled system is overly optimized for performance, leading to energy inefficiencies while a web browser is running. In this paper, we analyze the characteristics of web browsers, and investigate the cause of energy inefficiency in EAS-enabled mobile devices. We then propose a system, called WebTune, to improve the energy efficiency of mobile browsers. Exploiting the reinforcement learning technique, WebTune learns the optimal execution speed of the web browser's processes, and adjusts the speed at runtime, thus saving energy and ensuring the quality of service (QoS). WebTune is implemented on the latest Android-based smartphones, and evaluated with Alexa's top 200 websites. The experimental results show that WebTune reduced the device-level energy consumption of the Google Pixel 2 XL and Samsung Galaxy S9 Plus smartphones by 18.7-22.0% and 13.7-16.1%, respectively, without degrading the QoS.
Liang He (University of Colorado Denver); Linghe Kong, Ziyang Liu (Shanghai Jiao Tong University); Yuanchao Shu (Microsoft Research); Cong Liu (University of Texas at Dallas)
The automotive industry is increasingly employing software- based solutions to provide value-added features on vehicles, especially with the coming era of electric vehicles and autonomous driving. The ever-increasing cyber components of vehicles (i.e., computation, communication, and control), however, incur new risks of anomalies, as demonstrated by the millions of vehicles recalled by different manufactures. To mitigate these risks, we design B-Diag, a battery-based diagnostics system that guards vehicles against anomalies with a cyber-physical approach, and implement B-Diag as an add-on module of commodity vehicles attached to automotive batteries, thus providing vehicles an additional layer of protection. B-Diag is inspired by the fact that the automotive battery operates in strong dependency with many physical components of the vehicle, which is observable as correlations between battery voltage and the vehicle's corresponding operational parameters, e.g., a faster revolutions-per-minute (RPM) of the engine, in general, leads to a higher battery voltage. B-Diag exploits such physically-induced correlations to diagnose vehicles by cross-validating the vehicle information with battery voltage, based on a set of data-driven norm models constructed online. Such a design of B-Diag is steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life over 3 months, covering a total mileage of about 1, 400 miles. Besides the Crosstrek, we have also evaluated B-Diag with driving traces of a 2008 Honda Fit, a 2018 Volvo XC60, and a 2017 Volkswagen Passat, showing B-Diag detects vehicle anomalies with >86% (up to 99%) averaged detection rate.
Guang Wang, Xiuyuan Chen (Rutgers University); Fan Zhang (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences); Yang Wang (University of Science and Technology of China); Desheng Zhang (Rutgers University)
Due to the ever-growing concerns over air pollution and energy security, many cities have started to update their taxi fleets with electric ones. In this paper, we perform the first comprehensive measurement investigation called ePat to explore the evolving mobility and charging patterns of electric vehicles. Our ePat is based on 5-year 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolving process. Our ePat as a comprehensive investigation of the electric taxi network mobility and charging evolving has the potential to advance the understanding of the evolving patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.
Yu Yang, Xiaoyang Xie, Zhihan Fang (Rutgers University); Fan Zhang (Shenzhen Institute of Advanced Technology); Yang Wang (University of Science and Technology of China); Desheng Zhang (Rutgers University)
Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantly, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. We compared VeMo with state-of-the-art benchmark mobility models, and the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.