Same App, Different Behaviors: Uncovering Device-specific Behaviors in Android Apps

• URL: http://arxiv.org/abs/2406.09807v1
• Date: Fri, 14 Jun 2024 07:58:22 GMT
• Title: Same App, Different Behaviors: Uncovering Device-specific Behaviors in Android Apps
• Authors: Zikan Dong, Yanjie Zhao, Tianming Liu, Chao Wang, Guosheng Xu, Guoai Xu, Haoyu Wang,
• Abstract summary: We present the first large-scale empirical study of device-specific behaviors in real-world Android apps. By examining the distribution of device-specific behaviors, our analysis revealed that apps within the Chinese third-party app market exhibit more relevant behaviors compared to their counterparts in Google Play. Beyond common behaviors like issue fixes and feature adaptations, we observed 33 aggressive apps, including popular ones with millions of downloads.
• Score: 16.015694809763538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Android ecosystem faces a notable challenge known as fragmentation, which denotes the extensive diversity within the system. This issue is mainly related to differences in system versions, device hardware specifications, and customizations introduced by manufacturers. The growing divergence among devices leads to marked variations in how a given app behaves across diverse devices. This is referred to as device-specific behaviors. In this work, we present the first large-scale empirical study of device-specific behaviors in real-world Android apps. We have designed a three-phase static analysis framework to accurately detect and understand the device-specific behaviors. Upon employing our tool on a dataset comprising more than 20,000 apps, we detected device-specific behaviors in 2,357 of them. By examining the distribution of device-specific behaviors, our analysis revealed that apps within the Chinese third-party app market exhibit more relevant behaviors compared to their counterparts in Google Play. Additionally, these behaviors are more likely to feature dominant brands that hold larger market shares. Reflecting this, we have classified these device-specific behaviors into 29 categories based on implemented functionalities, providing structured insight into these behaviors. Beyond common behaviors like issue fixes and feature adaptations, we observed 33 aggressive apps, including popular ones with millions of downloads, abusing system properties of customized ROMs to obtain user-unresettable identifiers without requiring permission, substantially impacting user privacy. Finally, we investigated the origins of device-specific behaviors, revealing significant challenges developers face in implementing them comprehensively. Our research sheds light on the promising but less touched research direction of device-specific behaviors, benefiting community stakeholders.

Related papers

• Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions [18.294511216241805]
  This paper provides empirical evidence on how user mobility and behaviour affect mobile gaze tracking accuracy. Head distance, head pose, and device orientation are key factors affecting accuracy. Findings highlight the need for more robust, adaptive eye-tracking systems.
  arXiv  Detail & Related papers  (2025-02-14T21:44:52Z)
• A Tale of Three Location Trackers: AirTag, SmartTag, and Tile [0.8062368743143389]
  Bluetooth Low Energy (BLE) location trackers are popular consumer devices for monitoring personal items. This study presents a comprehensive analysis of three major players in the market: Apple's AirTag, Samsung's SmartTag, and Tile. Our methodology combines controlled experiments -- with a known large distribution of location-reporting devices -- as well as in-the-wild experiments.
  arXiv  Detail & Related papers  (2025-01-29T07:16:29Z)
• Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection [50.55317257140427]
  A strategy used by malicious actors is to "live off the land," where benign systems are used and repurposed for the malicious actor's intent. We show that this is plausible via YARA rules, which use human-written signatures to detect specific malware families. By extracting sub-signatures from publicly available YARA rules, we assembled a set of features that can more effectively discriminate malicious samples.
  arXiv  Detail & Related papers  (2024-11-27T17:03:00Z)
• Detecting Android Malware by Visualizing App Behaviors from Multiple Complementary Views [28.69137642535078]
  We propose and implement LensDroid, a novel technique that detects Android malware by visualizing app behaviors from multiple complementary views. Our goal is to harness the power of combining deep learning and software visualization to automatically capture and aggregate high-level features that are not inherently linked.
  arXiv  Detail & Related papers  (2024-10-08T16:00:27Z)
• Demystifying Device-specific Compatibility Issues in Android Apps [10.12578477875438]
  We conduct an empirical study of 197 DSC issues collected from 94 open-source repositories on GitHub. We introduce a new perspective for comprehending these issues by categorizing them into two principal groups, Functionality Breaks, and OEM Features. The different nature of functionality break issues and OEM feature issues lead to unique challenges in addressing them.
  arXiv  Detail & Related papers  (2024-08-03T15:34:58Z)
• Towards a Fair Comparison and Realistic Design and Evaluation Framework of Android Malware Detectors [63.75363908696257]
  We analyze 10 influential research works on Android malware detection using a common evaluation framework. We identify five factors that, if not taken into account when creating datasets and designing detectors, significantly affect the trained ML models. We conclude that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results.
  arXiv  Detail & Related papers  (2022-05-25T08:28:08Z)
• Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
  This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits. Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
  arXiv  Detail & Related papers  (2022-03-14T17:05:59Z)
• Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data [64.48836187884325]
  Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment.
  arXiv  Detail & Related papers  (2021-02-09T15:48:35Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)
• TAO: A Large-Scale Benchmark for Tracking Any Object [95.87310116010185]
  Tracking Any Object dataset consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. We ask annotators to label objects that move at any point in the video, and give names to them post factum. Our vocabulary is both significantly larger and qualitatively different from existing tracking datasets.
  arXiv  Detail & Related papers  (2020-05-20T21:07:28Z)

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.