政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/77178

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/77178

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题名:	用虛擬機內省技術強化OpenStack雲端安全機制 Enhancing OpenStack Cloud Security with Virtual Machine Introspection
作者:	李彥亨 Lee, Yen Heng
贡献者:	蔡瑞煌郁方 Tsaih, Rua Huan Yu, Fang 李彥亨 Lee, Yen Heng
关键词:	虛擬機內省雲端安全惡意程式行為 VMI Cloud Security Malware behavior
日期:	2015
上传时间:	2015-08-03 13:21:02 (UTC+8)
摘要:	如今，我們能夠受益於各式各樣的雲端服務（如Google及Amazon）全歸功於虛擬化技術的成熟。隨著雲端服務使用量的劇增，雲端安全的議題也不容忽視。傳統上使用網路型及主機型的入侵防衛系統來作雲端安全防護，但隨著虛擬化技術的發展，虛擬機內省機制為基底的防禦機制有著絕對於傳統入侵防衛系統優越的獨立性及可見度並逐漸成為主流的防禦機制。我們研究提倡的雲端防禦系統框架（VISO）便是以虛擬機內省機制為基底以及透過行為模式辨識的方式作惡意行為偵測且富有可擴增性，並強調所有的解決方案皆為開源的，也是為何我們以OpenStack作為我們的雲端環境防護系統的實驗環境。關於我們的實驗研究方法，我們採用監督式與非監督式的神經網路來作惡意程式行為分析。而所有惡意程式皆從官方OWL網站取得，並以防毒軟體作標籤的動作。目的是想要確認是否能夠透過同種類且已知的惡意程式行為模式去辨識出未知的同種類惡意程式行為。 Today, we attributes it to virtualization technology that the application of cloud computing is so well-developed that the world-wide famous company can make use of this technique to reap the profits, just likes Google and Amazon etc. While cloud service bringing kinds of benefit to system vendors and cloud tenants, cloud security is exposed to many threats. Traditionally, two main kinds of intrusion detection system (IDS) are host-based IDS (HIDS) and network-based IDS (NIDS). With virtualization technology development, virtual machine monitor (VMM) based IDS is superior to HIDS and NIDS both on isolation and visibility properties as far as cloud security concerned. We address a cloud security protection framework, called Virtualization Introspection System for OpenStack (VISO), to strengthen OpenStack security defensive mechanism. VISO has some following characteristics. (1) VMI based monitoring mechanism (2) behavior-based analysis (3) elastic to expand system functionality and easy to operate (4) all apparatuses in VISO are free on Internet that is why we also choose the most famous private cloud solution, OpenStack, to deploying cloud environment. About our experiment method, we using supervised and unsupervised artificial technology algorithm to analyze behaviors monitored in a sandbox environment. All malwares are downloaded from OWL Taiwan official malware knowledge base and labeled by anti-virus scanner. The purpose is to see how effective the features of behaviors collected by VISO can recognize the same family malwares. Detecting unknown malware variants previously not recognized by commercial anti-virus software by training the same family known malware samples.
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描述:	碩士國立政治大學資訊管理研究所 102356044
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0102356044
数据类型:	thesis
显示于类别:	[資訊管理學系] 學位論文

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