Title: GuCA-KFDCN: gull cruise attack optimised hybrid kernel filter enabled deep learning model for attack detection and mitigation in cloud computing environment

Authors: Yogesh B. Sanap; Pushpalata G. Aher

Addresses: Department of Computer Science and Engineering, Sandip University, Nashik, Maharashtra 422213, India ' Department of Computer Science and Engineering, Sandip University, Nashik, Maharashtra 422213, India

Abstract: In a cloud computing environment, resources are provided as services over the internet, eliminating the need for significant upfront capital expenditure. However, distributed denial of service (DDoS) attacks creates a considerable threat to this availability, making detection a critical aspect. These attacks can disrupt access, undermining the trust and reliability of cloud services. The conventional approaches employed for DDoS attack detection pose significant challenges regarding overfitting issues, computational complexity, and limited generalisability. As a result, to mitigate these challenges this research offers a Gull cruise attack optimised HybridKernel filter enabled deep convolutional neural network (GuCA-KFDCN) model. The utilisation of hybrid kernel filters integrates three different kernel functions, which effectively capture the complex attack patterns. Furthermore, the gull cruise attack optimisation (GuCAO) algorithm refines the performance of the model by optimising the parameters of the proposed model, ensuring robust performance. In addition, the GuCAO algorithm effectively chooses optimal key values for oversampling, which improves detection performance. The experimental outcomes show the efficacy of the proposed model interms of sensitivity of 95.29%, accuracy of 96.84%, and specificity of 97.74% for training percentage 80.

Keywords: deep convolutional neural network; cloud computing; gull cruise attack optimisation; GuCAO; distributed denial of service attack; hybridkernel filter.

DOI: 10.1504/IJCC.2025.148722

International Journal of Cloud Computing, 2025 Vol.14 No.3, pp.262 - 289

Received: 16 Oct 2024
Accepted: 10 Mar 2025
Published online: 21 Sep 2025 *

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