Title: A novel intelligent-based intrusion detection and prevention system in the cloud using deep learning with meta-heuristic strategy

Authors: Doddi Srilatha; N. Thillaiarasu

Addresses: School of Computing and Information Technology, REVA University, Bengaluru, 560064, India; Department of CSE-AIML, Sreenidhi Institute of Science and Technology, Hyderabad, 501301, India ' School of Computing and Information Technology, REVA University, Bengaluru, 560064, India

Abstract: Cloud computing serves diverse options for end-users to minimise costs, and services are easily accessible through online platforms. While the users access the services remotely, the attackers launch cyber-attacks to disrupt the services. Cloud security analysts treat the security of the cloud as a potential area of research to minimise the impacts of abnormal behaviour. One of the potential solutions to detect attacks is the development of the next-generation intrusion detection and prevention system (IDPS). Hence, this paper proposes an efficient IDPS using a hybridised model known as hybrid firebug-squirrel swarm algorithm-based ensemble classifiers (HF-SSA-EC). Initially, the NSL-KDD cup 1999 dataset is considered for experimental analysis. The efficient features are extracted via restricted Boltzmann machines (RBM) layers of the deep belief network (DBN) model. The extracted features are submitted to the ensemble classifiers (ECs), which use naive Bayes (NB), support vector machines (SVM), deep neural networks (DNN), and recurrent neural networks (RNN) for identifying the intrusions. EC parameter optimisation using a hybridised HF-SSA meta-heuristic improves performance. Finally, the prevention model eliminates malicious nodes from detected intrusions. Meta-heuristic clustering is used in the preventative model. The experimental results reveal that the recommended IDPS outperforms existing models.

Keywords: intrusion detection and prevention system; IDPS; cloud computing; restricted Boltzmann machines; RBM; deep feature extraction; firebug swarm optimisation; FSO; squirrel search algorithm.

DOI: 10.1504/IJDMB.2025.147032

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.3, pp.241 - 277

Received: 06 Aug 2023
Accepted: 01 Dec 2023
Published online: 10 Jul 2025 *

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