针对恶意软件数据漂移检测的规避与投毒攻击的实证分析
Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection
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中文总结 AI 辅助
本研究实证分析了针对恶意软件数据漂移检测器与恶意软件分类器组合的规避、投毒攻击，发现数据漂移检测器的独特特性会使针对恶意软件分类器的攻击产生不同效果。
中文摘要 AI 辅助
由于恶意软件演化导致的概念漂移对恶意软件分类构成挑战，因此开发了基于机器学习的数据漂移检测工具以缓解该问题。这些数据漂移检测器的设计目的与采用的技术，均与恶意软件分类器存在差异。尽管针对基于机器学习的恶意软件分类器的规避与投毒攻击可导致恶意软件样本被误分类，但目前尚不清楚此类攻击如何同时作用于数据漂移检测器与恶意软件分类器。本研究调查了规避与投毒攻击对数据漂移检测器及恶意软件分类器的影响，证明了数据漂移检测器的独特特性会导致针对恶意软件分类器的攻击对其产生不同的效果。
英文摘要
As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem. These data drift detector tools are designed for a different purpose and built with different techniques compared to malware classifiers. Although evasion and poisoning attacks against machine learning-based malware classifiers can cause misclassification of malware samples, it is not clear how these attacks work against data drift detectors and malware classifiers in combination. This work investigates the effect of evasion and poisoning attacks on the data drift detector along with the malware classifier. We demonstrate how unique characteristics of data drift detectors cause attacks against malware classifiers to work differently against them.
