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Leave Me Alone: App-Level Protection against Runtime Information Gathering on Android

Published: 17 May 2015 Publication History

Abstract

Stealing of sensitive information from apps is always considered to be one of the most critical threats to Android security. Recent studies show that this can happen even to the apps without explicit implementation flaws, through exploiting some design weaknesses of the operating system, e.g., Shared communication channels such as Bluetooth, and side channels such as memory and network-data usages. In all these attacks, a malicious app needs to run side-by-side with the target app (the victim) to collect its runtime information. Examples include recording phone conversations from the phone app, gathering WebMD's data usages to infer the disease condition the user looks at, etc. This runtime-information-gathering (RIG) threat is realistic and serious, as demonstrated by prior research and our new findings, which reveal that the malware monitoring popular Android-based home security systems can figure out when the house is empty and the user is not looking at surveillance cameras, and even turn off the alarm delivered to her phone. To defend against this new category of attacks, we propose a novel technique that changes neither the operating system nor the target apps, and provides immediate protection as soon as an ordinary app (with only normal and dangerous permissions) is installed. This new approach, called App Guardian, thwarts a malicious app's runtime monitoring attempt by pausing all suspicious background processes when the target app (called principal) is running in the foreground, and resuming them after the app stops and its runtime environment is cleaned up. Our technique leverages a unique feature of Android, on which third-party apps running in the background are often considered to be disposable and can be stopped anytime with only a minor performance and utility implication. We further limit such an impact by only focusing on a small set of suspicious background apps, which are identified by their behaviors inferred from their side channels (e.g., Thread names, CPU scheduling and kernel time). App Guardian is also carefully designed to choose the right moments to start and end the protection procedure, and effectively protect itself against malicious apps. Our experimental studies show that this new technique defeated all known RIG attacks, with small impacts on the utility of legitimate apps and the performance of the OS. Most importantly, the idea underlying our approach, including app-level protection, side-channel based defense and lightweight response, not only significantly raises the bar for the RIG attacks and the research on this subject but can also inspire the follow-up effort on new detection systems practically deployable in the fragmented Android ecosystem.

Cited By

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  • (2023)The Danger of Minimum Exposures: Understanding Cross-App Information Leaks on iOS through Multi-Side-Channel LearningProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3616655(281-295)Online publication date: 15-Nov-2023
  • (2022)Scrutinizing Privacy Policy Compliance of Virtual Personal Assistant AppsProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3560416(1-13)Online publication date: 10-Oct-2022
  • (2021)Machine Learning–based Cyber Attacks Targeting on Controlled InformationACM Computing Surveys10.1145/346517154:7(1-36)Online publication date: 18-Jul-2021
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  1. Leave Me Alone: App-Level Protection against Runtime Information Gathering on Android

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      cover image Guide Proceedings
      SP '15: Proceedings of the 2015 IEEE Symposium on Security and Privacy
      May 2015
      923 pages
      ISBN:9781467369497

      Publisher

      IEEE Computer Society

      United States

      Publication History

      Published: 17 May 2015

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      • (2023)The Danger of Minimum Exposures: Understanding Cross-App Information Leaks on iOS through Multi-Side-Channel LearningProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security10.1145/3576915.3616655(281-295)Online publication date: 15-Nov-2023
      • (2022)Scrutinizing Privacy Policy Compliance of Virtual Personal Assistant AppsProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3560416(1-13)Online publication date: 10-Oct-2022
      • (2021)Machine Learning–based Cyber Attacks Targeting on Controlled InformationACM Computing Surveys10.1145/346517154:7(1-36)Online publication date: 18-Jul-2021
      • (2019)Detecting (absent) app-to-app authentication on cross-device short-distance channelsProceedings of the 35th Annual Computer Security Applications Conference10.1145/3359789.3359814(328-338)Online publication date: 9-Dec-2019
      • (2019)Exploiting Sound Masking for Audio Privacy in SmartphonesProceedings of the 2019 ACM Asia Conference on Computer and Communications Security10.1145/3321705.3329799(257-268)Online publication date: 2-Jul-2019
      • (2019)MaMaDroidACM Transactions on Privacy and Security10.1145/331339122:2(1-34)Online publication date: 9-Apr-2019
      • (2019)A Survey on Various Threats and Current State of Security in Android PlatformACM Computing Surveys10.1145/330128552:1(1-35)Online publication date: 13-Feb-2019
      • (2018)SCAnDroidProceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks10.1145/3212480.3212506(224-235)Online publication date: 18-Jun-2018
      • (2018)Towards Dynamically Monitoring Android Applications on Non-rooted Devices in the WildProceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks10.1145/3212480.3212504(212-223)Online publication date: 18-Jun-2018
      • (2018)TruZ-DroidProceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3210240.3210338(14-27)Online publication date: 10-Jun-2018
      • Show More Cited By

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