基于蜜罐技术的分布式、高扩展性广域网攻击感知与复杂分析框架

Daniel Fraunholz, Marc Zimmermann, S. D. Antón, Jörg Schneider, H. Dieter Schotten
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引用次数: 17

摘要

最近,互联性的增加导致僵尸网络中支持物联网的设备数量不断增加。这种僵尸网络目前被用于大规模的DDoS攻击。为了跟踪这些恶意活动,蜜罐已被证明是一个至关重要的工具。我们开发并建立了一个分布式和高度可扩展的WAN蜜罐,并附带了一个后端基础设施,用于对收集的数据进行复杂的处理。为了使处理后的数据易于理解,我们设计了一个图形化的前端,显示从数据中获得的所有相关信息。我们将短时间内在一个源中发起的攻击分组为会话。这丰富了数据,可以进行更深入的分析。我们生成了常见的统计数据,如用户名、密码、用户名/密码组合、密码长度、原始国家等。从收集到的信息中,我们能够识别用于暴力登录攻击的常用字典和其他更复杂的统计数据,如每个会话的登录尝试次数和攻击效率。
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Distributed and highly-scalable WAN network attack sensing and sophisticated analysing framework based on Honeypot technology
Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.
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