ARMOUR US: Android Runtime Zero-permission Sensor Usage Monitoring from User Space

• URL: http://arxiv.org/abs/2507.02177v1
• Date: Wed, 02 Jul 2025 22:18:54 GMT
• Title: ARMOUR US: Android Runtime Zero-permission Sensor Usage Monitoring from User Space
• Authors: Yan Long, Jiancong Cui, Yuqing Yang, Tobias Alam, Zhiqiang Lin, Kevin Fu,
• Abstract summary: This work investigates how to monitor access to Android zero-permission sensors which could cause privacy leakage to users.<n>We develop ARMOUR for user-space runtime monitoring, leveraging the intrinsic sampling rate variation and convergence behaviors of Android.<n>Our evaluation with 1,448 commercial applications shows the effectiveness of ARMOUR in detecting sensor usage in obfuscated code.
• Score: 29.326546732781267
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work investigates how to monitor access to Android zero-permission sensors which could cause privacy leakage to users. Moreover, monitoring such sensitive access allows security researchers to characterize potential sensor abuse patterns. Zero-permission sensors such as accelerometers have become an indispensable part of Android devices. The critical information they provide has attracted extensive research investigating how data collectors could capture more sensor data to enable both benign and exploitative applications. In contrast, little work has explored how to enable data providers, such as end users, to understand sensor usage. While existing methods such as static analysis and hooking-based dynamic analysis face challenges of requiring complicated development chains, rooting privilege, and app-specific reverse engineering analysis, our work aims to bridge this gap by developing ARMOUR for user-space runtime monitoring, leveraging the intrinsic sampling rate variation and convergence behaviors of Android. ARMOUR enables privacy-aware users to easily monitor how third-party apps use sensor data and support security researchers to perform rapid app-agnostic sensor access analysis. Our evaluation with 1,448 commercial applications shows the effectiveness of ARMOUR in detecting sensor usage in obfuscated code and other conditions, and observes salient sensor abuse patterns such as 50% of apps from seemingly sensor-independent categories accessing data of multiple zero-permission sensors. We analyze the impact of Android's recent policy changes on zero-permission sensors and remaining technical and regulatory problems.

Related papers

• SensorLM: Learning the Language of Wearable Sensors [50.95988682423808]
  We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language.<n>We introduce a hierarchical caption generation pipeline designed to capture statistical, structural, and semantic information from sensor data.<n>This approach enabled the curation of the largest sensor-language dataset to date, comprising over 59.7 million hours of data from more than 103,000 people.
  arXiv  Detail & Related papers  (2025-06-10T17:13:09Z)
• Entropy Collapse in Mobile Sensors: The Hidden Risks of Sensor-Based Security [1.6267479602370545]
  We systematically analyse the entropy of mobile sensor data across four diverse datasets spanning multiple contexts.<n>Our results demonstrate that adversaries may feasibly predict sensor signals through an exhaustive, brute-force exploration of the entire measurement space.
  arXiv  Detail & Related papers  (2025-02-13T17:50:58Z)
• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• Stream-based perception for cognitive agents in mobile ecosystems [0.7865191493201839]
  We present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements.
  arXiv  Detail & Related papers  (2024-01-24T17:14:50Z)
• Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities [66.4525391417921]
  We design and evaluate a multi-sensor drone detection system. Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest. The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
  arXiv  Detail & Related papers  (2022-07-05T10:00:58Z)
• Bayesian Imitation Learning for End-to-End Mobile Manipulation [80.47771322489422]
  Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities. We show that using the Variational Information Bottleneck to regularize convolutional neural networks improves generalization to held-out domains. We demonstrate that our method is able to help close the sim-to-real gap and successfully fuse RGB and depth modalities.
  arXiv  Detail & Related papers  (2022-02-15T17:38:30Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Radar Voxel Fusion for 3D Object Detection [0.0]
  This paper develops a low-level sensor fusion network for 3D object detection. The radar sensor fusion proves especially beneficial in inclement conditions such as rain and night scenes.
  arXiv  Detail & Related papers  (2021-06-26T20:34:12Z)
• Domain and Modality Gaps for LiDAR-based Person Detection on Mobile Robots [91.01747068273666]
  This paper studies existing LiDAR-based person detectors with a particular focus on mobile robot scenarios. Experiments revolve around the domain gap between driving and mobile robot scenarios, as well as the modality gap between 3D and 2D LiDAR sensors. Results provide practical insights into LiDAR-based person detection and facilitate informed decisions for relevant mobile robot designs and applications.
  arXiv  Detail & Related papers  (2021-06-21T16:35:49Z)
• A First Look at Privacy Analysis of COVID-19 Contact Tracing Mobile Applications [3.592774078025348]
  The outbreak of COVID-19 in December 2019 has seen a surge of the mobile applications for tracing, tracking and isolating the persons showing COVID-19 symptoms. The use of embedded sensors could disclose private information of the users thus potentially bring threat to the privacy and security of users. This paper analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life.
  arXiv  Detail & Related papers  (2020-06-23T21:57:00Z)
• RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning [7.359795285967954]
  In most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. We propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them.
  arXiv  Detail & Related papers  (2020-04-19T03:52:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.