利用硬件性能计数器检测内核级rootkit

Baljit Singh, Dmitry Evtyushkin, J. Elwell, Ryan D. Riley, I. Cervesato
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引用次数: 81

摘要

最近的工作研究了使用硬件性能计数器(hpc)来检测系统上运行的恶意软件。这些工作收集各种应用程序(包括恶意和非恶意)的hpc痕迹,然后应用机器学习来训练检测器来区分良性应用程序和恶意软件。在这项工作中,我们对使用机器学习和hpc的特定恶意软件子集的适用性进行了更全面的分析:内核rootkits。我们设计了五个综合rootkit,每个都提供一个单一的rootkit功能,并在执行每个rootkit的同时收集其对特定基准应用程序的影响的HPC跟踪。然后,我们应用机器学习特征选择技术,以确定最相关的hpc检测这些rootkit。我们确定了16个对基于钩子的根的检测有用的hpc,并且还发现使用直接内核对象操作(DKOM)的rootkit不会显著影响hpc。然后,我们使用这些合成的rootkit痕迹来训练一个检测系统,该系统能够检测以前未见过的新rootkit,准确率超过99%。我们的研究结果表明,hpc有可能成为一种有效的rootkit检测工具,即使是针对以前未被检测器发现的新rootkit。
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 Detection of Kernel-Level Rootkits Using Hardware Performance Counters
Recent work has investigated the use of hardware performance counters (HPCs) for the detection of malware running on a system. These works gather traces of HPCs for a variety of applications (both malicious and non-malicious) and then apply machine learning to train a detector to distinguish between benign applications and malware. In this work, we provide a more comprehensive analysis of the applicability of using machine learning and HPCs for a specific subset of malware: kernel rootkits. We design five synthetic rootkits, each providing a single piece of rootkit functionality, and execute each while collecting HPC traces of its impact on a specific benchmark application. We then apply machine learning feature selection techniques in order to determine the most relevant HPCs for the detection of these rootkits. We identify 16 HPCs that are useful for the detection of hooking based roots, and also find that rootkits employing direct kernel object manipulation (DKOM) do not significantly impact HPCs. We then use these synthetic rootkit traces to train a detection system capable of detecting new rootkits it has not seen previously with an accuracy of over 99%. Our results indicate that HPCs have the potential to be an effective tool for rootkit detection, even against new rootkits not previously seen by the detector.
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