基于性能计数器的在线恶意软件检测可行性研究

J. Demme, Matthew Maycock, J. Schmitz, Adrian Tang, A. Waksman, S. Sethumadhavan, S. Stolfo
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引用次数: 409

摘要

计算机在任何领域的扩散都伴随着该领域恶意软件的扩散。系统,包括最新的移动平台,充斥着病毒、rootkit、间谍软件、广告软件和其他类型的恶意软件。尽管反病毒软件已经存在,但恶意软件的威胁仍然存在,而且还在不断增长,因为有无数种方法可以破坏反病毒(AV)软件。事实上,今天的攻击者利用反病毒软件中的漏洞侵入系统。在本文中,我们研究了利用现有的性能计数器在硬件上构建恶意软件检测器的可行性。我们发现来自性能计数器的数据可用于识别恶意软件,并且我们的检测技术对恶意软件程序中的微小变化具有鲁棒性。因此,在检查了Android、ARM和Intel Linux平台上的恶意软件家族中的一小部分变体后,我们可以在该家族中检测到许多变体。此外,我们提出的硬件修改允许恶意软件检测器在系统软件下安全运行,从而为比软件反病毒更简单、更少错误的反病毒实现奠定了基础。结合起来,硬件反病毒技术的鲁棒性和安全性有可能推进最先进的在线恶意软件检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the feasibility of online malware detection with performance counters
The proliferation of computers in any domain is followed by the proliferation of malware in that domain. Systems, including the latest mobile platforms, are laden with viruses, rootkits, spyware, adware and other classes of malware. Despite the existence of anti-virus software, malware threats persist and are growing as there exist a myriad of ways to subvert anti-virus (AV) software. In fact, attackers today exploit bugs in the AV software to break into systems. In this paper, we examine the feasibility of building a malware detector in hardware using existing performance counters. We find that data from performance counters can be used to identify malware and that our detection techniques are robust to minor variations in malware programs. As a result, after examining a small set of variations within a family of malware on Android ARM and Intel Linux platforms, we can detect many variations within that family. Further, our proposed hardware modifications allow the malware detector to run securely beneath the system software, thus setting the stage for AV implementations that are simpler and less buggy than software AV. Combined, the robustness and security of hardware AV techniques have the potential to advance state-of-the-art online malware detection.
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