POFVC: A Probabilistic Optimized Feature Voting Classification Framework for Intrusion Detection in Cloud Computing Environments

doi:10.23940/ijpe.26.10.p5.601609

Abstract

Abstract: Cloud computing has become the fundamental platform for offering scalable and on-demand computing services. However, the widespread use of cloud computing technology makes it vulnerable to cyber-attacks. Conventional intrusion detection techniques are prone to low detection accuracy because of redundant feature selection and higher computation complexity. In this paper, a Probabilistic Optimized Feature Voting Classification (POFVC) model is proposed to address the problem of intrusion detection in cloud computing. The proposed POFVC model incorporates data pre-processing, optimized feature selection, weighted feature voting, and probabilistic classification to increase attack detection efficiency and minimize the impact of irrelevant features on the system. The relevance of the features is calculated by means of Information Gain, Mutual Information, and Gini Importance, which allows selecting the most relevant network attributes. The proposed approach is evaluated using the UNSW-NB15 standard dataset and compared with conventional machine learning algorithms based on Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The experimental results show that the proposed model performs better in terms of accuracy and computational complexity reduction.

Key words: cloud computing, intrusion detection system (IDS), probabilistic optimized feature voting classification (POFVC), feature selection, probabilistic classification, machine learning, cloud security, UNSW-NB15 dataset

Ashima Jain, Ashima Narang, and Manju. POFVC: A Probabilistic Optimized Feature Voting Classification Framework for Intrusion Detection in Cloud Computing Environments [J]. Int J Performability Eng, 2026, 22(10): 601-609.

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