博碩士論文 108522075 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊工程學系zh_TW
DC.creator賴映岑zh_TW
DC.creatorYing-Cen Laien_US
dc.date.accessioned2021-9-2T07:39:07Z
dc.date.available2021-9-2T07:39:07Z
dc.date.issued2021
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=108522075
dc.contributor.department資訊工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract在雲端運算蓬勃發展下,彈性雲之營運方式被廣泛使用,透過根據用戶服務級別協議(SLA)提供 QoS,使服務更能按需求部署及彈性擴展。然而此種營運機制可能會受到針對雲服務之 Economic Denial of Sustainability(EDoS)攻擊,表面上對服務提出合法請求,利用服務自動擴展機制,造成系統持續擴展資源,讓用戶需要支付龐大的資源使用費。而本論文提出一個機制,利用基於 Dynamic Time Warping 的 K-means 分群演算法對用戶流量進行分群,並分析服務資源使用率和用戶流量之間的時間分布,來區分合法用戶及惡意用戶,並限制惡意用戶存取服務,來達到偵測 Yo-Yo 攻擊以及防禦之效果。zh_TW
dc.description.abstractAs development of cloud computing grows rapidly, the operation with Elastic Cloud is widely used. By providing QoS according to the user service level agreement (SLA), the service can be deployed and expanded flexibly on demand. However, this kind of operation may suffer Economic Denial of Sustainability (EDoS) attacks against cloud services. On the surface, legal requests are sent to the service, cause the system to continue to expand resources with auto-scaling mechanism. Let users need to pay for a huge usage fee. This paper proposes a mechanism that uses the K-means clustering algorithm based on Dynamic Time Warping to cluster users’ traffic, and analyzes the time distribution between system resource usage and user traffic to distinguish legitimate users from malicious users. Then restrict malicious users to access the Service. Keep the service from the threat of Yo-Yo attack.en_US
DC.subjectEDoS攻擊zh_TW
DC.subjectYo-Yo攻擊zh_TW
DC.subject流量分析zh_TW
DC.subjectK-means clusteringzh_TW
DC.subjectDynamic Time Warpingzh_TW
DC.subjectEDoS attacken_US
DC.subjectYo-Yo attacken_US
DC.subjectTraffic analysisen_US
DC.subjectK-means clusteringen_US
DC.subjectDynamic Time Warpingen_US
DC.title針對EDoS攻擊偵測及防禦之研究zh_TW
dc.language.isozh-TWzh-TW
DC.titleA Study of Detection and Defense for EDoS Attacken_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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