Mind Your Own Business - A Longitudinal Study of Threats and Vulnerabilities in Enterprises.

NDSS(2019)

引用 37|浏览99
暂无评分
摘要
Enterprises own a significant fraction of the hosts connected to the Internet and possess valuable assets, such as financial data and intellectual property, which may be targeted by attackers. They suffer attacks that exploit unpatched hosts and install malware, resulting in breaches that may cost millions in damages. Despite the scale of this phenomenon, the threat and vulnerability landscape of enterprises remains under-studied. The security posture of enterprises remains unclear, and it's unknown whether enterprises are indeed more secure than consumer hosts. To address these questions, we perform the largest and longest enterprise security study so far. Our data covers nearly 3 years and is collected from 28K enterprises, belonging to 67 industries, which own 82M client hosts and 73M public-facing servers. Our measurements comprise of two parts: an analysis of the threat landscape and an analysis of the enterprise vulnerability patching behavior. The threat landscape analysis studies the encounter rate of malware and PUP in enterprise client hosts. It measures, among others, that 91%-97% of the enterprises, 13%-41% of the hosts, encountered at least one malware or PUP file over the length of our study; that enterprises encounter malware much more often than PUP; and that some industries like banks and consumer finances achieve significantly lower malware and PUP encounter rates than the most-affected industries. The vulnerability analysis examines the patching of 12 client-side and 112 server-side applications in enterprise client hosts and servers. It measures, among others, that it takes over 6 months on average to patch 90% of the population across all vulnerabilities in the 12 client-side applications; that enterprise hosts are faster to patch vulnerabilities compared to consumer hosts; and that the patching of server applications is much worse than the patching of client-side applications.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要