steeæ lergon:一个在Android应用程序中注入串谋恶意负载的框架

Rosangela Casolare, Giovanni Ciaramella, F. Martinelli, F. Mercaldo, A. Santone
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引用次数: 1

摘要

移动恶意软件的数量在不断增长,其复杂性也在不断增加。恶意软件的作者一直在寻找新的方法来逃避反恶意软件控制。反恶意软件无法检测到零日恶意软件,因为要检测到恶意行为,他们需要知道它的签名,但要获得这些信息,恶意软件必须已经广泛传播。此外,反恶意软件能够一次扫描一个应用程序:由于这个原因,一种以串通攻击为特征的恶意软件,在这种攻击中,恶意行为被分成两个(或更多)应用程序,无法被识别。为了证明当前反恶意软件机制在识别共谋攻击方面的有效性,我们在本文中提出了SteælErgon框架,旨在向两个或多个不同的Android应用程序注入恶意有效载荷。显然,一旦将构成合谋攻击的所有应用程序安装到受感染的设备中,恶意有效载荷将被执行。具体来说,steekæ lergon能够注入合谋的恶意有效载荷攻击外部存储,使攻击者能够捕获存储在受感染设备中的敏感和私人信息。通过将生成的串通应用程序提交给不同的79种反恶意软件进行实验分析,表明当前的检测机制无法检测到此类威胁。为了加强对串通攻击的研究,我们免费发布了SteælErgon,可在以下url中用于研究目的:https://github.com/vigimella/StealErgon。
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SteælErgon: A Framework for Injecting Colluding Malicious Payload in Android Applications
Mobile malware is growing in number and its complexity is constantly increasing. Malware authors are continuously looking new ways to elude anti-malware controls. Anti-malware are not able to detect zero-day malware, because to detect malicious behaviour they need to know its signature, but to have this information the malware must already be widespread. Furthermore, anti-malware are able to scan one application at a time: for this reason a type of malware characterized by the colluding attack, where the malicious action is split in two (or more) applications, can not be recognised. To demonstrate the ineffectiveness of current anti-malware mechanisms in recognizing colluding attacks, in this paper we propose SteælErgon, a framework aimed to inject a malicious payload in two or more different Android applications. Clearly the malicious payload will be executed once all the applications composing the collusive attacks are installed into the infected device. In detail, SteælErgon is able to inject a collusive malicious payload attacking the external storage, allowing the attacker to catch sensitive and private information stored into the infected device. We perform an experimental analysis by submitting the generated colluding application to different 79 anti-malware, by showing that current detection mechanism are not able to detect this kind of threat. To boost research in focusing the attention in colluding attacks we freely release SteælErgon, is available for research purposes at the following url: https://github.com/vigimella/StealErgon.
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