焦点:大规模的恶意软件潜在客户生成

Fabian Kaczmarczyck, Bernhard Grill, L. Invernizzi, Jennifer Pullman, Cecilia M. Procopiuc, David Tao, B. Benko, Elie Bursztein
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引用次数: 5

摘要

恶意软件是当今网络安全的主要威胁之一,其应用范围从网络钓鱼邮件到勒索软件和木马。由于恶意软件威胁的绝对规模和多样性,将其作为一个整体进行打击是不切实际的。相反,政府和公司已经建立了专门的团队来识别、优先处理和删除直接影响其人口或业务模式的特定恶意软件家族。根据我们的调查,对最令人不安的恶意软件家族进行识别和优先排序(称为恶意软件搜索)是一项耗时的活动,占典型威胁情报研究人员工作时间的20%以上。为了节省这一宝贵的资源,并扩大团队对用户在线安全的影响,我们提出了Spotlight,一个大型恶意软件生成框架。Spotlight首先根据第一方和第三方威胁情报,筛选大型恶意软件数据集,以删除已知的恶意软件家族。然后,它将剩余的恶意软件聚类到可能未被发现的家族中,并根据其潜在的业务影响对其进行优先级排序,以供进一步调查。我们在67M恶意软件样本上对Spotlight进行了评估,结果表明它可以产生纯度超过99%(即同质性)的最高优先级集群,这比更简单的方法和先前的工作要高。为了展示Spotlight的有效性,我们将其应用于搜索真实数据的广告欺诈恶意软件。利用Spotlight的输出,威胁情报研究人员能够快速识别出三个大型僵尸网络,这些僵尸网络会进行广告欺诈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spotlight: Malware Lead Generation at Scale
Malware is one of the key threats to online security today, with applications ranging from phishing mailers to ransomware and trojans. Due to the sheer size and variety of the malware threat, it is impractical to combat it as a whole. Instead, governments and companies have instituted teams dedicated to identifying, prioritizing, and removing specific malware families that directly affect their population or business model. The identification and prioritization of the most disconcerting malware families (known as malware hunting) is a time-consuming activity, accounting for more than 20% of the work hours of a typical threat intelligence researcher, according to our survey. To save this precious resource and amplify the team’s impact on users’ online safety we present Spotlight, a large-scale malware lead-generation framework. Spotlight first sifts through a large malware data set to remove known malware families, based on first and third-party threat intelligence. It then clusters the remaining malware into potentially-undiscovered families, and prioritizes them for further investigation using a score based on their potential business impact. We evaluate Spotlight on 67M malware samples, to show that it can produce top-priority clusters with over 99% purity (i.e., homogeneity), which is higher than simpler approaches and prior work. To showcase Spotlight’s effectiveness, we apply it to ad-fraud malware hunting on real-world data. Using Spotlight’s output, threat intelligence researchers were able to quickly identify three large botnets that perform ad fraud.
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