Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network
Swati Nautiyal1, C Rama Krishna2, Shruti Wadhwa3
1Swati Nautiyal, PG Scholar, Department of Computer Science and Engineering, NITTTR, Chandigarh, India.
2C. Rama Krishna, Department of Computer Science and Engineering, NITTTR, Chandigarh, India.
3Shruti Wadhwa, Department of Computer Applications, Post Graduate Govt. College, Chandigarh, India.
Manuscript received on 12 August 2019 | Revised Manuscript received on 17 August 2019 | Manuscript published on 30 August 2019 | PP: 3415-3421 | Volume-8 Issue-10, August 2019 | Retrieval Number: J96800881019/19©BEIESP | DOI: 10.35940/ijitee.J9680.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: IEconomic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.
Key words: Artificial Neural Network, Cloud Computing, EDoS attack, Genetic algorithm.
Scope of the Article: Cloud Computing