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Mean Availability Parameter-Based DDoS Detection Mechanism for Cloud Computing Environments

机译:云计算环境的基于基于参数的基于参数的DDOS检测机制

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Trustworthiness of edge routers and clients plays a significant role in a cloud environment for ensuring reliable packet delivery. Trust of clients depends on the level of cooperation attributed by them for ensuring seamless service and on the support rendered by them for the sake of their neighbouring clients towards the core objective of reliable data dissemination. The level of collaboration between clients is highly influenced by distributed denial of service (DDoS) attacks as they directly influence the performance of cloud computing environment by preventing them from involving in normal data transactions that could result in reduced throughput and packet delivery rate. A mean availability parameter-based DDoS detection mechanism (MAPDDM) is contributed for handling the impacts induced by DDoS towards the dynamic clients of the subnet. The performance of MAPDDM is analysed by varying the size of subnets and number of attackers under the dynamic influence of varying traffic request using CloudSim. The simulation results infer that MAPDDM is phenomenal in sustaining the trust value of clients to a maximum of 82% even when the amount of traffic is varied.
机译:边缘路由器和客户的可信度在云环境中发挥着重要作用,以确保可靠的数据包交付。客户的信任依赖于他们归功于确保无缝服务的合作水平,并为其邻近客户提供可靠的数据传播的核心目标。客户端之间的协作水平受到分布式拒绝服务(DDOS)攻击的影响,因为它们通过阻止它们涉及可能导致吞吐量和分组传递率的正常数据事务直接影响云计算环境的性能。均值的基于参数的DDOS检测机制(MAPDDM)有助于处理DDO引起的影响朝向子网的动态客户端。通过使用CloudSIM改变不同的业务请求的动态影响,通过改变子网的大小和攻击者的数量来分析MAPDDM的性能。仿真结果推断,MapDDM是在使客户的信任价值持续到最高82%即使变化的数量也是如此的现象。

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