an approach to detect DDoS attacks in the cloud computing environment using entropy and particle swarm optimization

Subject Areas : Electronics Engineering

mehdi  asayeshjoo ¹ (computer of department,azad university,booshehr,iran)
Mehdi  sadeghzadeh ² (Department of Computer Engineering, Faculty of Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran)
maziyar  ganjoo ³ (computer of department ,azad university ,booshehr,iran)

Received: 2021-03-10 Accepted : 2021-06-30 Published : 2021-06-22

Keywords: Attack detection, cloud computing, particle swarm optimization, Entropy,

Abstract :

Cloud computing is an emerging technology that is widely used to provide computing, data storage services and other remote resources over the Internet. Availability of cloud services is one of the most important concerns of cloud service providers. While cloud services are mainly transmitted over the Internet, they are prone to various attacks that may lead to the leakage of sensitive information. Distributed DDoS attack is known as one of the most important security threats to the cloud computing environment. This attack is an explicit attempt by an attacker to block or deny access to shared services or resources in a cloud environment. This paper discusses a hybrid approach to dealing with DDoS attack in the cloud computing environment. This method highlights the importance of effective feature-based selection methods and classification models. Here, an entropy-based approach and particle swarm optimization to counter these attacks in a cloud computing environment is presented. Classification on high-dimensional data typically requires feature selection as a pre-processing step to reduce the dimensionality. However, effective features selecting is a challenging task, which in this paper uses particle swarm optimization. Here, the proposed classification model is developed based on the use of a balanced binary search tree and dictionary data structure. The simulation is based on the NSL-KDD and CICDDoS2019 datasets, which prove the superiority of the proposed method with an average detection accuracy of 99.84% over the AGA and E-SVM algorithms.

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