BotMAD:基于DNS流量分析的僵尸网络恶意活动检测器

Pooja Sharma, Sanjeev Kumar, Neeraj Sharma
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引用次数: 9

摘要

僵尸网络是受感染计算机的集合,即僵尸电脑的集合,这些电脑由一个人或一个被称为botmaster的组织远程控制。近年来,僵尸网络通过植入某些技术来隐藏自己,如快速通量或DGA算法来生成域名,从而在本质上变得更加隐蔽。一般来说,僵尸网络可以分为两大类:一类是利用IP协议,另一类是利用DNS协议进行通信。使用DNS协议的僵尸程序恶意软件被设计成在很长一段时间内不受影响。一旦它们收到来自僵尸管理员的命令,它们就开始响应以执行进一步的可操作命令来执行垃圾邮件或DDoS攻击。针对这一问题,提出了基于DNS流量模式分析的僵尸网络恶意活动检测,用于检测基于IP协议的利用技术无法检测到的这类僵尸网络家族,这些僵尸网络家族的IP可能被僵尸主机通过快速流量或其他技术改变,使其具有隐身性。介绍了一种自动化的DNS流量分析器和检测器BotMAD,它通过检测来自网络轨迹的DNS报文,自动检测出恶意IP/域对。进一步将DNSBL数据库的提要与系统集成,通过Intel关键堆栈API获取恶意域的记录,丰富数据库。为了验证系统的准确性,使用了两个数据集-一个是在蜜罐上捕获的机器人恶意软件的网络痕迹,第二个是用于验证的域信誉引擎。最后,我们得出结论,所开发的框架在僵尸网络域检测方面取得了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BotMAD: Botnet malicious activity detector based on DNS traffic analysis
Botnet is a collection of infected computers i.e. collection of zombie PCs which are remotely controlled by a single person or a group so called botmaster. In the recent years, botnets are becoming stealthier in nature by implanting certain techniques to hide themselves such as fast flux or DGA algorithms to generate the domain names. Generally, the class of botnet can be categorized into two major class-one which exploiting the IP protocol and another is using the DNS protocol for communications. The bot malwares who are using the DNS protocol are designed to remain unaffected over a long period of time. Once they receive the commands from the botmaster, they start to response to execute further actionable commands to perform SPAMs or DDoS attacks. To address such issues, BotMAD-Botnet Malicious Activity Detection based on DNS traffic pattern analysis is presented to detect such class of botnet family which are not detected by IP protocol based exploiting technique because IP may be changed by the botmaster by using fast flux or other techniques to make them stealth in nature. BotMAD — an automated DNS traffic Analyzer and Detector is introduced which automatically detect the malicious IP/Domain pair by inspecting the DNS packets from the network traces. Further the feed of DNSBL database is integrated with the system by fetching the records of malicious domains through Intel critical-stack API to enrich the database. To validate the accuracy of the system, two data sets are used-one is network traces of bot malwares captured on honeypots and second one domain reputation engines for validation. In the end, we conclude that the developed framework is giving the promising results in the form of botnet domain detection.
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