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題名 VISO: Characterizing malicious behaviors of virtual machines with unsupervised clustering
作者 曾宇瑞
郁方
Li, Yen Han
Tzeng, Yeu Ruey
Yu, Fang
貢獻者 資管系
關鍵詞 Computer crime; Intrusion detection; Java programming language; Malware; Personal computers; Semantics; Cloud securities; Clustering; Detecting malicious behaviors; Intrusion Detection Systems; On-line monitoring system; Supervised classification; Virtual machine introspection; Virtual machine management; Cloud computing
日期 2016-02
上傳時間 1-Sep-2017 10:06:43 (UTC+8)
摘要 Cloud computing has become one of the most dominant computation platforms in recent years. Security threats could be one of the major stunning blocks on this evolution road. While system vendors and cloud tenants benefit much from sharing resources in the cloud environment, security breaches can cause more significant damages of the cloud ecosystem than personal computers. Virtualization techniques facilitate the movement of intrusion detection system to cloud-host operating systems with virtual machine management by observing behaviors of virtual machines (VMs). However, a VM-based detection system inherits the semantic gap problem: it is needed the ability to reveal (malicious) behaviors of VMs from observed data. We propose an automatic and systematic analysis framework for charactering malware behaviors using unsupervised clustering. This framework consists of three phases: (1) unsupervised clustering on behaviors of VMs, (2) supervised classification rule derivation, and (3) online system detection. Specifically, we collect and cluster system call distributions of VMs within a small period as samples, identify clusters that contain only samples from malicious VMs, and derive detection rules by extracting features of these malicious clusters. VMs that have been observed their system call distributions falling into a malicious cluster are considered to be malicious. We have integrated the presented framework with OpenStack and develop a prototype online monitoring system, called VISO. We conduct several experiments against common attacks, showing the effectiveness of VISO on clustering, classifying and detecting malicious behaviors of VMs.
關聯 Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015 , 34-41
資料類型 conference
DOI http://dx.doi.org/10.1109/CloudCom.2015.19
dc.contributor 資管系
dc.creator (作者) 曾宇瑞zh_TW
dc.creator (作者) 郁方zh_TW
dc.creator (作者) Li, Yen Hanen_US
dc.creator (作者) Tzeng, Yeu Rueyen_US
dc.creator (作者) Yu, Fangen_US
dc.date (日期) 2016-02
dc.date.accessioned 1-Sep-2017 10:06:43 (UTC+8)-
dc.date.available 1-Sep-2017 10:06:43 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2017 10:06:43 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112487-
dc.description.abstract (摘要) Cloud computing has become one of the most dominant computation platforms in recent years. Security threats could be one of the major stunning blocks on this evolution road. While system vendors and cloud tenants benefit much from sharing resources in the cloud environment, security breaches can cause more significant damages of the cloud ecosystem than personal computers. Virtualization techniques facilitate the movement of intrusion detection system to cloud-host operating systems with virtual machine management by observing behaviors of virtual machines (VMs). However, a VM-based detection system inherits the semantic gap problem: it is needed the ability to reveal (malicious) behaviors of VMs from observed data. We propose an automatic and systematic analysis framework for charactering malware behaviors using unsupervised clustering. This framework consists of three phases: (1) unsupervised clustering on behaviors of VMs, (2) supervised classification rule derivation, and (3) online system detection. Specifically, we collect and cluster system call distributions of VMs within a small period as samples, identify clusters that contain only samples from malicious VMs, and derive detection rules by extracting features of these malicious clusters. VMs that have been observed their system call distributions falling into a malicious cluster are considered to be malicious. We have integrated the presented framework with OpenStack and develop a prototype online monitoring system, called VISO. We conduct several experiments against common attacks, showing the effectiveness of VISO on clustering, classifying and detecting malicious behaviors of VMs.
dc.format.extent 208 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015 , 34-41en_US
dc.subject (關鍵詞) Computer crime; Intrusion detection; Java programming language; Malware; Personal computers; Semantics; Cloud securities; Clustering; Detecting malicious behaviors; Intrusion Detection Systems; On-line monitoring system; Supervised classification; Virtual machine introspection; Virtual machine management; Cloud computing
dc.title (題名) VISO: Characterizing malicious behaviors of virtual machines with unsupervised clusteringen_US
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1109/CloudCom.2015.19
dc.doi.uri (DOI) http://dx.doi.org/10.1109/CloudCom.2015.19