Network dialog minimization and network dialog diffing: two novel primitives for network security applications

M. Zubair Rafique, Juan Caballero, Christophe Huygens, W. Joosen
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引用次数: 10

Abstract

In this work, we present two fundamental primitives for network security: network dialog minimization and network dialog diffing. Network dialog minimization (NDM) simplifies an original dialog with respect to a goal, so that the minimized dialog when replayed still achieves the goal, but requires minimal network communication, achieving significant time and bandwidth savings. We present network delta debugging, the first technique to solve NDM. Network dialog diffing compares two dialogs, aligns them, and identifies their common and different parts. We propose a novel dialog diffing technique that aligns two dialogs by finding a mapping that maximizes similarity. We have applied our techniques to 5 applications. We apply our dialog minimization approach for: building drive-by download milkers for 9 exploit kits, integrating them in a infrastructure that has collected over 14,000 malware samples running from a single machine; efficiently measuring the percentage of popular sites that allow cookie replay, finding that 31% do not destroy the server-side state when a user logs out and that 17% provide cookies that live over a month; simplifying a cumbersome user interface, saving our institution 3 hours of time per year and employee; and finding a new vulnerability in a SIP server. We apply our dialog diffing approach for clustering benign (F-Measure = 100%) and malicious (F-Measure = 87.6%) dialogs.
网络对话框最小化和网络对话框区分:网络安全应用程序的两个新基本要素
在这项工作中,我们提出了网络安全的两个基本要素:网络对话最小化和网络对话区分。网络对话框最小化(NDM)简化了原始对话框的目标,以便在重播时最小化的对话框仍然可以实现目标,但需要最少的网络通信,从而节省大量的时间和带宽。我们提出了网络增量调试,这是解决NDM的第一种技术。网络对话区分对两个对话进行比较,对它们进行对齐,并识别它们的共同部分和不同部分。我们提出了一种新的对话区分技术,该技术通过寻找最大相似性的映射来对齐两个对话。我们已经将我们的技术应用到5个应用中。我们将对话框最小化方法应用于:为9个漏洞利用工具包构建驱动下载挤奶器,将它们集成在一个基础设施中,该基础设施收集了超过14,000个恶意软件样本,从一台机器上运行;有效地测量了允许cookie重播的流行网站的百分比,发现31%的网站在用户退出时不会破坏服务器端状态,17%的网站提供一个多月的cookie;简化繁琐的用户界面,为单位每年节省3小时的时间和员工;以及在SIP服务器上发现新的漏洞。我们将对话区分方法应用于良性(F-Measure = 100%)和恶意(F-Measure = 87.6%)对话的聚类。
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