Please use this identifier to cite or link to this item: http://localhost:8081/jspui/handle/123456789/17740
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dc.contributor.authorChowdhury, Utpal-
dc.date.accessioned2025-07-04T13:05:48Z-
dc.date.available2025-07-04T13:05:48Z-
dc.date.issued2015-05-
dc.identifier.urihttp://localhost:8081/jspui/handle/123456789/17740-
dc.description.abstractDistributed Denial of Service (DDoS) attacks are serious threat to Cloud. These attacks consume large amount of resources and increase the service usage cost by significant factor. Due to multitenancy and self-provisioning properties of Cloud, traditional DDoS detection techniques cannot be directly applied to it. Hence there is a need of Cloud specific DDoS detection framework. In this work, we have proposed a behavior based framework uses Euclidean Distance for finding the hidden correlation between different attributes of the same network traffic record. After extracting the hidden correlations, Mahalanobis Distance is used for finding correlation between them. A collaborative training and warning sharing mechanism has also been proposed for adopting any changes of network behavior in Cloud and for preventing the occurrence of same types of attack in future. Performance improvement on threshold point provides extra flexibility to the network administrator for selecting an efficient threshold point. Our system performance evaluation shows very high detection rate with few false positive and false negative rate.en_US
dc.description.sponsorshipINDIAN INSTITUTE OF TECHNOLOGY ROORKEEen_US
dc.language.isoenen_US
dc.publisherIIT ROORKEEen_US
dc.subjectDistributed Denial of Service (DDoS)en_US
dc.subjectClouden_US
dc.subjectEuclidean Distanceen_US
dc.subjectMahalanobis Distanceen_US
dc.titleDETECTION OF DDoS ATTACKS IN CLOUD ENVIRONMENT USING COLLABORATIVE TRAININGen_US
dc.typeOtheren_US
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