Improved DDoS Attacks detection using Hybrid Statistical Model and sparse representation for Cloud Computing

Abstract

While hosting various cloud based information technology facilities by handling various assets on the internet, Cloud service accessibility has remained one of the chief concerns of cloud service providers (CSP). Several security concerns associated to cloud computing service simulations, and cloud’s major qualities contribute towards its susceptibility of security threats related with cloud service availability, the liability of internet, and the dispense behavior of cloud computing. Distributed Denial of Service (DDoS) attacks is one of the main advanced threats that occur to be extremely problematic and stimulating to stand owing towards its dispersed behavior and resulted in cloud service interruption. Although there exist amount of interruption recognition resolutions anticipated by various investigation groups, there exists not at all such a faultless result that avoids the DDoS attack and cloud service providers (CSP) are presently consuming various detection resolutions by assuring that their product stays well protected. The features of DDoS attack consuming various forms with dissimilar scenarios make it problematic to identify. Inspecting and analyzing various surviving DDoS detecting methods contrary to several factors is accomplished by this paper. To enhance the system performance further, sparse based data optimization is proposed to remove the redundant data. This enhancement reduced the execution time of the system by0.2%.