Cloud computing is a technology which not only gained advantages from ascendant technologies, but also suffered from its security breaches, of which availability is the most serious security issue. Distributed Denial of Service (DDoS) is a kind of resource-availabilityrelated attack launched with the aim of subverting the Data Centre (DC) for resource unavailability to the legitimate clients. In this paper, we propose 'Multilevel Thrust Filtration (MTF) mechanism' as a solution, which authenticates the incoming requesters and detects the different types of DDoS attacks at different levels to detect the most intensive attack at an early stage to prevent the unnecessary traffic reaching the DC. A hybrid solution is proposed to detect four different kinds of attacks that have been taken into consideration. Profit analysis proved that the proposed mechanism is deployable at an attack-prone DC for resource protection, which would eventually result in beneficial service at slenderised revenue.
Distributed Denial of Service (DDoS) attack launched in Cloud computing environment resulted in loss of sensitive information, Data corruption and even rarely lead to service shutdown. Entropy based DDoS mitigation approach analyzes the heuristic data and acts dynamically according to the traffic behavior to effectively segregate the characteristics of incoming traffic. Heuristic data helps in detecting the traffic condition to mitigate the flooding attack. Then, the traffic data is analyzed to distinguish legitimate and attack characteristics. An additional Trust mechanism has been deployed to differentiate legitimate and aggressive legitimate users. Hence, Goodput of Datacenter has been improved by detecting and mitigating the incoming traffic threats at each stage. Simulation results proved that the Enhanced Entropy approach behaves better at DDoS attack prone zones. Profit analysis also proved that the proposed mechanism is deployable at Datacenter for attack mitigation and resource protection which eventually results in beneficial service at slenderized revenue
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