声学窃听攻击调查:原理、方法和进展

IF 3.2 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS High-Confidence Computing Pub Date : 2024-05-18 DOI:10.1016/j.hcc.2024.100241
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引用次数: 0

摘要

在当今的信息时代,窃听已成为信息安全领域最严重的隐私威胁之一,如exodus间谍软件(Rudie等人,2021年)和pegasus间谍软件(Anatolyevich,2020年)。其中最主要的是声学窃听。声学窃听(George 和 Sagayarajan,2023 年)是一种利用麦克风、传感器或其他设备收集和处理声音信号并将其转换为可读信息的技术。尽管在这一领域已经做了很多研究,但鉴于技术的不断进步和窃听方法的快速发展,对这一技术的时效性仍然缺乏全面的调查。在本文中,我们对声学窃听进行了选择性概述,重点介绍了声学窃听的方法。具体来说,我们将声学窃听分为三类:基于运动传感器的声学窃听、基于光学传感器的声学窃听和基于射频的声学窃听。在这三个具有代表性的框架内,我们将根据它们使用的设备类型和各自的物理原理回顾声学窃听的成果。其次,我们还介绍了这些声学窃听方法的几个重要但具有挑战性的应用。此外,我们还从是否具有非侵入性、是否能实现无约束的文字窃听、是否使用了机器学习等多个角度,比较了符合实际场景中声学窃听要求的系统。我们文章的总体模板如下:首先,我们对现有的窃听技术进行了系统的回顾和分类,阐述了它们的工作机制,并给出了相应的公式。然后,对这些窃听方法进行对比分析,从多个维度评价每种方法的有效性和技术难度。除了对该领域的现状进行评估外,我们还讨论了当前的不足和挑战,并为声学窃听研究的未来发展指明了富有成效的方向。我们希望能继续激励研究人员朝这个方向努力。
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A survey of acoustic eavesdropping attacks: Principle, methods, and progress
In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.
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