Http based botnet detection using network traffic traces

学校代码： 10286 类号： TP393 密 级： 公开 UDC： 004.7 学 号： 119736 HTTP-BASED BOTNET DETECTION USING NETWORK TRAFFIC TRACES 研究生姓名： TRUONG DINH TU _ 导 师 姓 名： 程光 教授 申请学位类别 工学博士 一级学科名称 计算机科学与技术 论文答辩日期 2015 年 12 月 22 日_ 二级学科名称 计算机系统结构 学位授予日期 20 年 月 22 日_ 答辩委员会主席 陈鸣 教授 学位授予单位 _ 评 阅 东 南 大 学 人 _ 2015 年 12 月 22 日 博士学位论文 HTTP-BASED BOTNET DETECTION USING NETWORK TRAFFIC TRACES 专 业 名 称：计算机系统结构 研究生姓名：TRUONG DINH TU 导 师 姓 名： 程 光 教授 HTTP-BASED BOTNET DETECTION USING NETWORK TRAFFIC TRACES A Dissertation Submitted to Southeast University For the Academic Degree of Doctor of Engineering BY TRUONG DINH TU Supervised by Prof CHENG Guang School of Computer Science and Engineering Southeast University November 2015 摘要 摘要 僵尸网络已经成为当今 Internet 面临的最严重威胁之一，它们被作为高度 受控的平台用于进行大规模合作的网络攻击，如：分布式拒绝服务，垃圾邮件， 信息窃取等。因此，僵尸网络检测至关重要，安全研究人员已经提出了诸多有 效的僵尸网络检测方法。 然而，僵尸网络制作者仍不断开发新的技术来改进僵尸程序，以逃避安全 研究人员提出的检测方法。近年来，基于 HTTP 的僵尸网络愈加泛滥，已对众 多政府组织和工业机构造成巨大破坏。新一代的 HTTP 僵尸网络多采取 fastflux, domain-flux 或 DGA (Domain Generation Algorithmically)技术来逃避检测， 其中一些使用 domain-flux 技术来规避黑名单检测，而一些使用 fast-flux 技术 来隐藏真实的命令控制服务器位置。 因此， 本文主要研究目标是对使用 DGA, domain-flux 或 fast-flux 技术来 逃避检测的 HTTP 僵尸网络构建检测方案。为此，本文解决如下三个问题：（1） 研究在被管网或企业网中识别与检测感染 DGA 僵持程序的主机；（2）检测与 识别使用 domain-flux 或 DGA 技术的 C&C 服务器；（3）检测恶意的 fast-flux 服务网络。此 项的主要研究内容概括如下： 第一个问题是如何在被管网或企业网中识别出感染 DGA 僵持程序的主机。 为此，本文收集了多个知名的 domain-flux 或 DGA-bot 僵尸程序样本，如 Kraken, Zeus, Conficker, Bobax 和 Murofet。然后在虚拟机环境中执行这些样本 并获取相应的网络流量数据。通过检查和分析这些网络流量数据，本文发现这 I Southeast University, PhD Dissertation, Truong Dinh Tu 些僵尸程序样本在请求域名时呈现出相似的周期行为。另外 ，感染 domainflux 或 DGA-bot 僵尸程序的主机在查找 C&C 服务器时经常会请求大量的非存 在域名，且请求行为的周期时间间隔序列具有相似性。而一般的合法主机是不 会以相似的周期时间间隔序列来访问许多不同域名，并且产生大量的非存在域 名应答。这些相似行为仅发生在感染 DGA 僵持程序的主机上。因此，基于上 述特征，本文提出一种通过分析 DNS 请求时间间隔序列对的关联性来聚类相 似域名的方法, 即同一僵尸网络或 DGA 算法所产生的域名相似性的方法。实 验结果表明，相同 DGA 僵尸代码产生的域名会被划为同一类别中。请求某类 域名的主机则被标记为感染相应 domain-flux 或 DGA-bot 僵尸程序的主机。该 方法并不能适用于所有感染僵尸程序主机的检测。它只有效检测被管网内感染 domain-flux 或 DGA 类型僵尸程序主机。此项研究结果将有助于寻找新的 C&C 服务器检测方法，这也是本文今后的研究工作之一（第 章）。 第二个问题是如何检测出 domain-flux 或 DGA 僵尸网络的 C&C 服务器。 已有一些研究工作关注此问题[1-4]，而且这些方法也取得了一定效果。Yadav 等[1]给出了一种基于所有域名一元和二元语法分布的 DGA 僵尸网络 C&C 域 名检测方法。然而，该方法特别是检测 Kraken, Bobax 或 Murofet 僵尸网络产 生的域名时效果欠佳，因为这些僵尸网络产生的域名与正常域名在一元和二元 语法分布上没有较大差别。为克服此缺陷，本文工作目标是改进和扩展 Yadav 等[1]等人的工作。本文计算了正常域名的 n-grams (n=3, 4, 5)的发生频率，并 分别给每个 n-gram 评分。为区分一个域名是合法域名还是僵尸网络产生的域 名，本文提出了一种方法来测量域名期望分值，并且结合其他两个特征来输入 II Chapter Conclusion and Future Works important features, uses the popular machine-learning algorithms to train dataset, and shows which algorithms can be applied to detect botnet with the highest efficiency The experiments show that some MLAs give very good predictive results, such as the K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, Decision Trees (J48) and Random Forest classifiers However, comparing prediction performance of classifiers based on ROC curve and AUC values, the Decision Trees (J48) and Random Forest are two algorithms achieve the best performance for detecting botnet It can believe that our proposed approach is useful and can help security experts and organizations in their fight against cybercrime 6.2 Limitation and Future Work The effectiveness of the proposed method has been verified through the experimental results in the dissertation However, our work clearly has its own limitations and not comprehensive to detect all botnets on the Internet For example, for a flux domain to be detected, our method need to observe at a minimum one DNS message related to that domain Because if a flux domain is never queried by any of the users within the monitored network, our system will not be able to detect it In DGA-botnet detecting, we suppose that an attacker knows about the features that we are looking for in DNS traffic, she/he might try to evade detection by our proposed system To achieve this purpose, the attackers could try to improve their DGAs to avoid the specific features that we are looking for in DNS traffic For example, an attacker could create domain names that are comprised of a noun, verb or a combination of English language words These techniques generate domains, which attempt to exhibit lower entropy and higher ESOD making it harder to detect by our proposed method However, such domains often have a high probability of conflict with previously registered domains by legitimate users This is a selfdefeating act Therefore, the smart attackers are always thinking of ways how to 109 Southeast University, PhD Dissertation, Truong Dinh Tu design an algorithm to generate domains significantly different from human generated ones to avoid conflict, but still exhibit low entropy and high ESOD to be able to overcome our proposed method This is a new challenge for attackers to improve their DGA-bots to evade detection by our proposed system In the future work, we should collect other botnet samples, combine our current work with the lexical analysis methods in the field of natural language processing to improve the detection accuracy rate and to minimize the false positive rate For 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Accepted to publish [145] Arbor Networks, Inc ATLAS Summary Report: Global Fast Flux [EB/OL] (2014-01-01) [2015-09-20] http://atlas.arbor.net/summary/fastflux [146] Malware Domain List [EB/OL] (2014-01-01) http://www.malwaredomainlist.com/hostslist/hosts.txt [147] Malware Domains [EB/OL] (2014-01-01) http://mirror1.malwaredomains.com/files/domains.txt [148] PhishTank [EB/OL] (2014-01-01) http://data.phishtank.com/data/onlinevalid.csv [149] hpHosts [EB/OL] (2014-01-01) http://hphosts.gt500.org/hosts.txt [150] CyberCrime Tracker [EB/OL] (2014-01-01) http://cybercrimetracker.net/all.php 120 Acknowledgements Acknowledgements Foremost, I would like to express my most sincere thanks and gratitude to my advisor, Prof Cheng Guang, who led me on this journey, guided me towards the right direction, and provided me with insightful and inspiring advice during the four years at School of Computer Science and Engineering, Southeast University I have been extremely lucky to work with him He has had an enormous impact for me and set a benchmark for me to follow in my career I have learnt a lot during the time spent in his laboratory and had many lively discussions, which will benefit me in my later study I am also grateful to my dissertation committee members, Prof Gong Jian, Prof Ding Wei and Prof Wu Hua for their interest in my work Their insightful comments have significantly improved the quality of my work I would like to extend my thank-you to all my lab mates and Vietnamese students whom supporting me to much more through encouragement, idea, gathers information, taking extra dissection during my study I would particularly like to thank my family for their mental support and encouragement, my dear mother, my dear father, my sisters, and my younger brother Without their support and encouragement, I would not have the confidence and perseverance to go through this journey Finally, I am grateful to all those who devote much time to reading this dissertation and give me much advice, which will benefit me in my later study Again, thanks to all the people mentioned above! Nanjing, China November 2015 Truong Dinh Tu 121 Southeast University, PhD Dissertation, Truong Dinh Tu 122 List of Publications List of Publications Truong Dinh Tu and Cheng Guang " Detecting Bot-Infected Machines Based On Analyzing The Similar Periodic DNS Queries In Network Traffic," in Conference on Computing, Management and Educational Tech (ComManTel 2015), 2015 (EI index, Accepted) Truong Dinh Tu, Cheng Guang, Ahmad Jakalan, Guo Xiao Jun and Zhou Ai Ping "Detecting DGA-Based Botnet With DNS Traffic Analysis In Monitored Network," Journal of Internet Technology (JIT), 2015 (EI, SCI index, Accepted) Truong Dinh Tu, Cheng Guang, Guo Xiao Jun, and Pan Wu Bin, “Evil-hunter: a novel web shell detection system based on scoring scheme,” Journal of Southeast University (English Edition), vol 30, no 3, pp 278-284, 2014 (EI index) Truong Dinh Tu, Cheng Guang, Guo Xiao Jun, and Pan Wu Bin, "Webshell detection techniques in web applications," in International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp 1-7, 2014 (EI index) Truong Dinh Tu and Cheng Guang, "A novel bot identification method based on Domain-Flux traffic features", in The 16th Cross-Strait Conference on Information Technology (CSIT2014), 2014 Truong Dinh Tu, Cheng Guang, Guo Xiao Jun "Detecting Domain-Flux Botnet Based on DNS Traffic Features in Monitored Network," 2015 (SCI index, in progress review) Truong Dinh Tu, Cheng Guang "Detecting Fast-Flux Service Networks Uses Feature-Based Machine Learning Classification Techniques," China Communications (SCI index, in progress review) Guo Xiao Jun, Cheng Guang, Zhu Chen Gang, Truong Dinh Tu, and Zhou A.P, "Progress in research on active network flow watermark", Tongxin Xuebao/Journal on Communications, vol 35, pp 178-192, 2014 (EI index) Zhou Ai Ping, Cheng Guang, Guo Xiao Jun, Truong Dinh Tu and Zhu Cheng Gang "Heavy Hitter Identification Based on Adaptive Sampling with MapReduce", in International journal of Computer Systems Science and Engineering, 2015 (SCI index) 10 Guo Xiao Jun, Cheng Guang, Zhu Chen Gang, Zhou Ai Ping, Pan Wu Bin, and Truong Dinh Tu, "Make Your Webpage Carry Abundant Secret Information Unawarely," in High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), 2013 IEEE 10th International Conference on, pp 541-548, 2013 (EI index) 123 ...博士学位论文 HTTP- BASED BOTNET DETECTION USING NETWORK TRAFFIC TRACES 专 业 名 称：计算机系统结构 研究生姓名：TRUONG DINH TU 导 师 姓 名： 程 光 教授 HTTP- BASED BOTNET DETECTION USING NETWORK TRAFFIC TRACES A Dissertation... 2.1 Botnet Detection Techniques 21 2.1.1 Honeypots -based detection 21 2.1.2 Anomaly -based Detection 23 2.1.3 DNS -based Detection 23 2.1.4 Mining -based Detection. .. scale of network 2.1.4 Mining -based Detection One effective technique for botnet detection is to identify botnet C&C traffic However, botnet C&C traffic is difficult to detect In fact, since botnets