الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Nowadays, the online availability of internet resources has provided efficient means of information sharing. Cloud computing environments make available managed computer system resources to internet users and companies with options for processing, storage, management, as well as access to data and information within a certain server. However, several types of attacks can be targeting the cloud environment, among these types of attacks, a DDoS attack is considered the most common and dangerous type. Along these lines, in this work, we propose a protection system for securing the cloud computing environment against DDoS attack.Nevertheless, the proposed solution was designed based on the common and effective machine learning technique, which is called SVM, and is used for traffic detection and classification. In addition, an improved software agent was used as a complement to the SVM, for anomaly traffic control. The testing dataset was required to test and evaluate the performance of the proposed system. For that reason, the Coburg Intrusion Detection DataSets (CIDDS) was used. Furthermore, according to the obtained results, it was recognized that the developed system achieved the best results when compared with the related works in the literature with an accuracy of 99.6% in anomaly traffic classification and control.