多阶段多面物联网蜜罐生态系统

Armin Ziaie Tabari, Xinming Ou
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引用次数: 12

摘要

物联网(IoT)设备的快速增长使得了解它们面临的现实网络安全威胁变得至关重要。传统上,蜜罐被用作模拟网络上真实设备的诱饵,帮助研究人员/组织了解威胁的动态。蜜罐要产生有用的洞察力,一个关键条件是让攻击者相信它们是人类和组织使用的真实系统。然而,由于各种设备类型和物理连接的性质,物联网设备在这方面构成了独特的挑战。在这项工作中,我们(1)提出了一种创建多阶段多面蜜罐生态系统的方法,研究人员通过观察现实世界攻击者的行为,逐渐增加低交互物联网蜜罐的复杂性;(2)为物联网摄像头构建了一个低交互蜜罐,使研究人员能够具体了解攻击者在物联网摄像头设备上追求什么;(3)设计了一个代理实例,称为ProxyPot。它位于物联网设备和外部网络之间,帮助研究人员研究物联网设备的入站/出站通信。我们使用PorxyPot作为了解针对物联网摄像头的攻击的手段,并提高蜜罐的复杂性。我们部署蜜罐已经两年多了。我们的初步结果表明,我们能够在每个新阶段吸引越来越复杂的攻击数据。此外,我们捕获的活动似乎涉及直接的人类交互,而不是纯粹的自动化脚本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-phased Multi-faceted IoT Honeypot Ecosystem
The rapid growth of Internet of Things (IoT) devices makes it vitally important to understand real-world cybersecurity threats to them. Traditionally, honeypots have been used as decoys to mimic real devices on a network and help researchers/organizations understand the dynamic of threats. A crucial condition for a honeypot to yield useful insights is to let attackers believe they are real systems used by humans and organizations. However, IoT devices pose unique challenges in this respect, due to the large variety of device types and the physical-connectedness nature. In this work, we (1) presented an approach to create a multi-phased multi-faceted honeypot ecosystem, where researchers gradually increase the sophistication of a low-interaction IoT honeypot by observing real-world attackers' behaviors, (2) built a low-interaction honeypot for IoT cameras that allowed researchers to gain a concrete understanding of what attackers were going after on IoT camera devices, and (3) designed a proxy instance, called ProxyPot, that sits between IoT devices and the external network and helps researchers study the IoT devices' inbound/outbound communication. We used PorxyPot as a means to understanding attacks against IoT cameras and increasing the honeypot's sophistication. We deployed honeypots for more than two years. Our preliminary results showed that we were able to attract increasingly sophisticated attack data in each new phase. Moreover, we captured activities that appeared to involve direct human interactions rather than purely automated scripts.
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