论文标题
部分可观测时空混沌系统的无模型预测
MalGrid: Visualization Of Binary Features In Large Malware Corpora
论文作者
论文摘要
恶意软件的数量不断增加。尽管大多数新恶意软件是现有的恶意软件的修改,但其纯粹的数字却非常不知所措。在本文中,我们提出了一个新型系统,可将数百万个恶意软件可视化和映射到二维(2D)空间网格中的点。这使大型恶意软件数据集中的关系可视化,这些关系可用于开发分类解决方案,以快速筛选不同的恶意软件并提供情境意识。我们的方法链接了交互式显示中的两个可视化。我们的第一个观点是基于二进制特征表示恶意软件的二进制特征表示的尺寸投影,基于空间点的可视化相似性。我们的第二个基于空间网格的视图在其共享的基于二进制的视觉表示方面可以更好地了解所选恶意软件样本之间的相似性和差异。我们还提供了一个案例研究,其中包装对恶意软件数据的影响与包装算法的复杂性相关。
The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a novel system to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that can be used to develop triage solutions to screen different malware rapidly and provide situational awareness. Our approach links two visualizations within an interactive display. Our first view is a spatial point-based visualization of similarity among the samples based on a reduced dimensional projection of binary feature representations of malware. Our second spatial grid-based view provides a better insight into similarities and differences between selected malware samples in terms of the binary-based visual representations they share. We also provide a case study where the effect of packing on the malware data is correlated with the complexity of the packing algorithm.