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首页> 外文期刊>IEEE transactions on dependable and secure computing >Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
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Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

机译：是的，机器学习可以更安全！ Android恶意软件检测案例研究

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摘要

To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However, its security against well-crafted attacks has not only been recently questioned, but it has been shown that machine learning exhibits inherent vulnerabilities that can be exploited to evade detection at test time. In other words, machine learning itself can be the weakest link in a security system. In this paper, we rely upon a previously-proposed attack framework to categorize potential attack scenarios against learning-based malware detection tools, by modeling attackers with different skills and capabilities. We then define and implement a set of corresponding evasion attacks to thoroughly assess the security of Drebin, an Android malware detector. The main contribution of this work is the proposal of a simple and scalable secure-learning paradigm that mitigates the impact of evasion attacks, while only slightly worsening the detection rate in the absence of attack. We finally argue that our secure-learning approach can also be readily applied to other malware detection tasks.
机译：为了应对现代攻击日益增加的可变性和复杂性，机器学习已被广​​泛用作统计数据检测恶意软件的工具。但是，其针对精心设计的攻击的安全性不仅在最近受到了质疑，而且还显示出机器学习具有固有的漏洞，可以利用这些漏洞在测试时规避检测。换句话说，机器学习本身可能是安全系统中最薄弱的环节。在本文中，我们通过对具有不同技能和能力的攻击者进行建模，依靠先前提出的攻击框架对基于学习的恶意软件检测工具的潜在攻击场景进行分类。然后，我们定义并实施一组相应的规避攻击，以彻底评估Android恶意软件检测器Drebin的安全性。这项工作的主要贡献是提出了一种简单且可扩展的安全学习范例，该范例可以减轻规避攻击的影响，而在没有攻击的情况下只会稍微降低检测率。我们最终认为，我们的安全学习方法也可以很容易地应用于其他恶意软件检测任务。

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