在阳光的阴影下:了解和检测合法服务提供商网络上的防弹主机

Sumayah A. Alrwais, Xiaojing Liao, Xianghang Mi, Peng Wang, Xiaofeng Wang, Feng Qian, R. Beyah, Damon McCoy
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引用次数: 34

摘要

防弹主机(BPH)服务为犯罪行为者提供了能够应对非法活动投诉的技术基础设施,这是简化多种类型攻击的基本组成部分。坊间报道强调了这些BPH服务从低端提供商(托管isp、云托管和cdn)转售基础设施的新趋势,而不是从单一的BPH提供商那里。这使得许多先前检测BPH的方法变得不那么有效,因为基础设施不是高度集中在几个恶意自治系统(ase)中,而是灵活地分散在更大的提供商集合中,这些提供商混合了良性和恶意客户端。在本文中,我们首次对BPH服务的新趋势进行了系统的研究。通过收集和分析大量数据(整个Whois IPv4地址空间的25个快照,1.5 TB的被动DNS数据,以及来自几个黑名单源的纵向数据),我们能够识别出一组新特征,这些特征是子分配上BPH的独特特征,并且规避这些特征的代价很高。基于这些特征,我们训练了一个用于检测恶意子分配网络块的分类器,根据我们的评估,实现了98%的召回率和1.5%的错误发现率。使用我们的分类器的保守训练版本,我们扫描整个IPv4地址空间并检测到39K恶意网络块。这使我们能够对BPH服务生态系统进行大规模研究,从而揭示这种地下业务策略,包括网络块被回收的模式和恶意客户端被迁移到不同的网络块,以逃避基于IP地址的黑名单。我们的研究强调了敏捷BPH服务的趋势,并指出了检测和减轻这种新兴威胁的潜在方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Under the Shadow of Sunshine: Understanding and Detecting Bulletproof Hosting on Legitimate Service Provider Networks
BulletProof Hosting (BPH) services provide criminal actors with technical infrastructure that is resilient to complaints of illicit activities, which serves as a basic building block for streamlining numerous types of attacks. Anecdotal reports have highlighted an emerging trend of these BPH services reselling infrastructure from lower end service providers (hosting ISPs, cloud hosting, and CDNs) instead of from monolithic BPH providers. This has rendered many of the prior methods of detecting BPH less effective, since instead of the infrastructure being highly concentrated within a few malicious Autonomous Systems (ASes) it is now agile and dispersed across a larger set of providers that have a mixture of benign and malicious clients. In this paper, we present the first systematic study on this new trend of BPH services. By collecting and analyzing a large amount of data (25 snapshots of the entire Whois IPv4 address space, 1.5 TB of passive DNS data, and longitudinal data from several blacklist feeds), we are able to identify a set of new features that uniquely characterizes BPH on sub-allocations and that are costly to evade. Based upon these features, we train a classifier for detecting malicious sub-allocated network blocks, achieving a 98% recall and 1.5% false discovery rates according to our evaluation. Using a conservatively trained version of our classifier, we scan the whole IPv4 address space and detect 39K malicious network blocks. This allows us to perform a large-scale study of the BPH service ecosystem, which sheds light on this underground business strategy, including patterns of network blocks being recycled and malicious clients being migrated to different network blocks, in an effort to evade IP address based blacklisting. Our study highlights the trend of agile BPH services and points to potential methods of detecting and mitigating this emerging threat.
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