基于光传感器的移动设备隐蔽信道

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mila Dalla Preda , Claudia Greco , Michele Ianni , Francesco Lupia , Andrea Pugliese
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引用次数: 0

摘要

光传感器在移动设备中的广泛应用实现了从自动亮度控制到环境监测的各种功能。然而,由于不受限制的访问权限,这些传感器在安卓生态系统中也存在重大的安全和隐私风险。本文探讨了如何通过新颖的基于光的带外信道将光传感器数据用于隐蔽通信。我们开发了两种使用亮度值对数据进行编码和解码的方法--基线法和基于重置法。这些方法可以应对数据变化和传感器事件时间不可预测性带来的挑战。为了提高数据传输的准确性,我们的方法采用了一种新颖的策略来选择亮度参考序列,并利用基于均方误差的距离来进行解码。实验结果验证了我们方法的有效性及其在实际应用中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Light sensor based covert channels on mobile devices
The widespread adoption of light sensors in mobile devices has enabled functionalities that range from automatic brightness control to environmental monitoring. However, these sensors also present significant security and privacy risks within the Android ecosystem due to unrestricted access permissions. This paper explores how light sensor data can be used for covert communication through a novel, light-based out-of-band channel. We develop two approaches–Baseline and ResetBased–that use luminance values to encode and decode data. These methods tackle challenges that arise from data variability and the unpredictability of sensor event timings. To enhance data transmission accuracy, our methods employ a novel strategy for selecting luminance reference sequences and leverage mean-squared-error-based distance for decoding. Experimental results validate the effectiveness of our approaches and their potential for real-world applications.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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