克服gpu存在下基于hpc的加密劫持检测缺陷

Claudius Pott, Berk Gulmezoglu, T. Eisenbarth
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引用次数: 1

摘要

随着连接到互联网的设备数量的增加,针对这些设备的网络攻击数量也随之增加。攻击者可以采用几种策略,例如窃取受害者的知识产权或加密数据以索要解密赎金。在这项工作中,我们专注于检测所谓的加密劫持攻击,在这种攻击中,攻击者获得了对系统的访问权限,然后引入使用受害者设备的处理能力来挖掘加密货币的程序。这种攻击的存在并不是显而易见的,攻击者设法保持不被发现的时间越长,他们可以从受害者支付电费中获利的时间就越长。在这项研究中,我们结合了以前的方法来证明,通过利用Windows操作系统上的硬件性能计数器,可以以96%的准确率检测加密劫持攻击。此外,我们提出了一种方法来确定哪些性能事件导致最佳检测率,从而允许选择一些可以由现代消费者cpu同时监视的性能事件。在下一步中,我们展示了当攻击者从使用CPU资源切换到使用gpu进行挖掘任务时,基于CPU计数器的检测机制会失败。基于这些发现,我们改进了之前的检测方法,通过使用特定于gpu的指标扩展CPU性能计数器,从而使基于gpu的加密劫持攻击类的准确率达到99.86%。除了高检测率外,所提出的方法在监测整个系统时只会造成可忽略不计的性能损失,从而允许对运行系统进行连续监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Overcoming the Pitfalls of HPC-based Cryptojacking Detection in Presence of GPUs
With the rising number of devices connected to the internet, the number of cyber-attacks on these devices increases in parallel. There are several strategies that an attacker can pursue, like stealing intellectual property of a victim or encrypting data to demand ransom for the decryption. In this work, we are focusing on the detection of so called cryptojacking attacks, in which an attacker that gained access to a system, then introduces programs that use the processing power of the victim device to mine cryptocurrencies. The presence of such an attack is not obvious right away and the longer an attacker manages to remain undetected, the longer they can profit having the victim foot the power bill. In this study, we combine previous approaches to demonstrate that cryptojacking attacks can be detected with an accuracy of 96% by leveraging hardware performance counters on the Windows operating system. Further, we present a method to determine which performance events result in the best detection rates, thus allowing the selection of a few performance events that can be monitored simultaneously by modern consumer CPUs. In a next step, we show that the CPU counters-based detection mechanism fails when an attacker switches from using the CPU resources to GPUs for the mining tasks. Based on these findings we then improve the previous detection approaches by extending the CPU performance counters with GPU-specific metrics resulting in 99.86% accuracy for the GPU-based cryptojacking attack class. In addition to a high detection rate the presented approach only causes a negligible performance loss while monitoring the whole system, which allows for continuous monitoring of live systems.
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