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Minimizing Financial Cost of DDoS Attack Defense in Clouds With Fine-Grained Resource Management

机译:最大限度地减少云层中DDOS攻击防御的财务成本,具有细粒度的资源管理

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摘要

As the cloud systems gain in popularity, they suffer from cyber attacks. One of the most notorious cyber attacks is Distributed Denial of Service (DDoS) attack, which aims to drain the system resources so that the system becomes unresponsive to the genuine users. DDoS attack and defense essentially revolve around resource competition. Many efforts have been made from the perspective of resource investment and management. However, these defending schemes assume that the resources available to defend the attacks are unlimited without taking the financial cost into account. Such coarse-grained defense strategies could cause the problem of resource overprovisioning, which would incur unwanted extra costs to the defender. To tackle this issue, we systematically investigate the problem and propose a birth-death-based fine-grained resource management mechanism, which can both scale in/out and scale down/up. That is, the proposed mechanism adaptively selects the optimal resource leasing mode for cloud service customers so that they can defeat the DDoS attack with minimal financial cost. Extensive analyses and empirical data-based experiments are conducted. The results show both the effectiveness and efficiency of the proposed approach. Comparing to existing work, our proposal can averagely save 53.58% (up to 93.75%) of the cost for the attack defense.
机译:随着云系统的普及,他们遭受网络攻击。其中一个最臭名昭着的网络攻击是分布式拒绝服务(DDOS)攻击的攻击,这旨在耗尽系统资源,以便系统对真正用户无响应。 DDOS攻击和国防基本上围绕资源竞争。从资源投资和管理的角度取得了许多努力。但是,这些卫生方案假设可用于抵御攻击的资源无限制,而不考虑财务成本。这种粗暴的防御战略可能导致资源过度控制问题,这将导致后卫的不良额外成本。为了解决这个问题,我们系统地调查了这个问题并提出了一种基于出生死亡的细粒度资源管理机制,可以缩放/出来并缩小/缩小。也就是说,所提出的机制自适应地为云服务客户选择最佳资源租赁模式,以便以最小的财务成本击败DDOS攻击。进行广泛的分析和经验数据的实验。结果表明了所提出的方法的有效性和效率。与现有工作相比,我们的提案可以平均节省53.58%(高达93.75%)攻击防范费用。

著录项

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  • 作者单位

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Cyber Sci & Engn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Cyber Sci & Engn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Cyber Sci & Engn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Cyber Sci & Engn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China|Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China;

    Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cyber Phys Social Syst Lab Wuhan 430074 Peoples R China|St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada;

    Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Sch Cyber Sci & Engn Cluster & Grid Comp Lab Big Data Secur Engn Res C Wuhan 430074 Peoples R China;

    Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England;

    Univ Technol Sydney Sch Software Ultimo NSW 2007 Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Denial-of-service attack; Resource management; Computer crime; Big Data; Cloud computing; Proposals; Optimization; DDoS attacks; cloud security; resource management;

    机译:拒绝服务攻击;资源管理;计算机犯罪;大数据;云计算;提案;优化;DDOS攻击;云安全;资源管理;
  • 入库时间 2022-08-18 21:54:18

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