隐蔽入侵:通过隐蔽渠道描绘基于新颖规避攻击的安卓威胁

Sunil Gautam , Ketaki Pattani , Mohd Zuhair , Mamoon Rashid , Nazir Ahmad
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引用次数: 0

摘要

移动设备的隐私和安全问题对个人、团体、政府和企业都有重大影响。安卓操作系统通过对应用程序行为的限制,加强了对智能手机数据的保护。尽管如此,攻击者还是会进行系统的资源分析,并将隐私敏感信息从普通视线中转移出去。他们采用规避机制来躲避系统监控,并制造良性和非敏感通信的假象。此外,隐蔽渠道通过非标准方法促进信息传输,扩大了这些恶意活动的影响。本研究旨在揭示这些针对安卓系统的新型威胁。本研究深入探讨了危害用户敏感信息的安全和隐私攻击。该方法利用了规避概念,并采用了特定声音的隐蔽信道通信,特别是超声波信道。这项研究工作引入了新颖的规避攻击,即 "Prime-Composite Evasive Information Invasion (PCEII) "和 "File-lock-based Evasive Information Invasion (FEII)",这两种攻击都依赖于隐蔽信道通信。这些独特的攻击变种在嘈杂和非嘈杂环境中都能在几毫秒内成功规避用户数据,并且不会被反病毒卫士(AVG)、360 安全等杀毒机制和 TaintDroid、MockDroid 等最先进的工具发现。本文不仅评估了它们对信息隐私和安全的影响,还介绍了检测和减少它们的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covertvasion: Depicting threats through covert channels based novel evasive attacks in android

Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive Information Invasion (PCEII) and File-lock-based Evasive Information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation.

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