Open Access

ARTICLE

Adaptive Butterfly Optimization Algorithm (ABOA) Based Feature Selection and Deep Neural Network (DNN) for Detection of Distributed Denial-of-Service (DDoS) Attacks in Cloud

S. Sureshkumar^1,*, G .K. D. Prasanna Venkatesan², R. Santhosh³

1 Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India
2 Dean Engineering, Faculty Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India
3 Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, 641021, India

* Corresponding Author: S. Sureshkumar. Email:

Computer Systems Science and Engineering 2023, 47(1), 1109-1123. https://doi.org/10.32604/csse.2023.036267

Received 23 September 2022; Accepted 07 April 2023; Issue published 26 May 2023

Abstract

Cloud computing technology provides flexible, on-demand, and completely controlled computing resources and services are highly desirable. Despite this, with its distributed and dynamic nature and shortcomings in virtualization deployment, the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties. The Intrusion Detection System (IDS) is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources. DDoS attacks are becoming more frequent and powerful, and their attack pathways are continually changing, which requiring the development of new detection methods. Here the purpose of the study is to improve detection accuracy. Feature Selection (FS) is critical. At the same time, the IDS’s computational problem is limited by focusing on the most relevant elements, and its performance and accuracy increase. In this research work, the suggested Adaptive butterfly optimization algorithm (ABOA) framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase, that was motivated by this motive Candidates. Accurate classification is not compromised by using an ABOA technique. The design of Deep Neural Networks (DNN) has simplified the categorization of network traffic into normal and DDoS threat traffic. DNN’s parameters can be fine-tuned to detect DDoS attacks better using specially built algorithms. Reduced reconstruction error, no exploding or vanishing gradients, and reduced network are all benefits of the changes outlined in this paper. When it comes to performance criteria like accuracy, precision, recall, and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches. Hence the proposed ABOA + DNN is an excellent method for obtaining accurate predictions, with an improved accuracy rate of 99.05% compared to other existing approaches.

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