ICTACT Journal on Soft Computing

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Data Mining Approach and Security Over DDOS Attacks


Affiliations
1 School of Computing Science and Engineering, Galgotias University, India
2 Department of Computer Science, Gambella University, India
     

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The benefit of on-demand services is one of the most important benefits of using cloud computing; therefore, the payment method in the cloud environment is pay per use. This feature results in a new type of DDOS attack called Economic Denial of Sustainability (EDoS), where as a result of the attack the customer pays the cloud provider extra. Similar to other DDoS attacks, EDoS attacks are divided into different groups, such as bandwidth-consuming attacks, specific target attacks, and connections-layer-exhaustion attacks. In this study, we propose a novel system for detecting different types of EDoS attacks by developing a pro le that learns from normal and abnormal behaviors and classifies them. In this sense, the extra demanding resources are allocated only to VMs that are found to be in a normal situation and thus prevent attack and resource dissemination from the cloud environment.

Keywords

DDoS Attacks, EDoS Attacks, Cloud Computing, Machine Learning Detection.
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  • Data Mining Approach and Security Over DDOS Attacks

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Authors

M. Arvindhan
School of Computing Science and Engineering, Galgotias University, India
Bhanu Prakash Ande
Department of Computer Science, Gambella University, India

Abstract


The benefit of on-demand services is one of the most important benefits of using cloud computing; therefore, the payment method in the cloud environment is pay per use. This feature results in a new type of DDOS attack called Economic Denial of Sustainability (EDoS), where as a result of the attack the customer pays the cloud provider extra. Similar to other DDoS attacks, EDoS attacks are divided into different groups, such as bandwidth-consuming attacks, specific target attacks, and connections-layer-exhaustion attacks. In this study, we propose a novel system for detecting different types of EDoS attacks by developing a pro le that learns from normal and abnormal behaviors and classifies them. In this sense, the extra demanding resources are allocated only to VMs that are found to be in a normal situation and thus prevent attack and resource dissemination from the cloud environment.

Keywords


DDoS Attacks, EDoS Attacks, Cloud Computing, Machine Learning Detection.

References