论文标题
恶意软件检测的静态，动态和混合分析的比较
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection
论文作者
论文摘要
在这项研究中，我们比较了基于静态，动态和混合分析的恶意软件检测技术。具体来说，我们在静态和动态功能集上训练隐藏的马尔可夫模型（HMM），并比较了大量恶意软件系列的检测率。我们还考虑在训练阶段使用动态分析的混合情况，在检测阶段使用静态技术，反之亦然。在我们的实验中，完全动态的方法通常会产生最佳的检测率。我们讨论了这项研究对基于混合技术的恶意软件检测的含义。
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based on hybrid techniques.
