Adaptive Ensemble Models for Robust Intrusion Detection in Cloud Environment on Imbalanced Dataset

Abstract: The rapid development of Information storage and sharing technologies brings new challenges in protecting against network security attacks. In this study, ensemble learning models are evaluated to enhance the performance of a network intrusion detection system (NIDS) with three phases through machine learning approaches. In the first phase, the unbalanced dataset is processed through four re-sampling techniques, such as SMOTE, RUS, RUS+ROS, and RUS+SMOTE, for balancing treatment. In the second phase, Random Forest feature selection is imposed for these four balanced datasets. Finally, three Ensemble Models named as EM1, EM2 and EM3 are designed using six basic classifiers and thus evaluated. In earlier studies, the first and second phases were evaluated through an SVM binary classifier for four feature subsets. The four feature subsets are obtained through Random Forest feature selection with the four different thresholds of Cumulative Feature Importance Scores (CFIS) (85%, 90%, 95% and 99%). With the observation of the evaluated results, three challenges were identified: i) The highest accuracy obtained through the re-sampling method required maximum computational time. ii) Different thresholds of CFIS exhibit instability in performance metrics as well as computational times, even though the number of features is less. iii) The adopted multi-class SVM classifier’s efficiency to detect the attacks within minimum computational time and without compromising accuracy when compared to earlier works is yet to be ascertained. In this study, an attempt has been made to address these challenges with ensemble learning. Three ensemble models are chosen for the evaluation process conducted on the adopted CIIDS -2017 dataset. Finally, the comparative results are presented, and decisive discussions are carried out for implementing the prevention and mitigation algorithms by security professionals.

Swarnalatha K, Nirmalajyothi Narisetty, Gangadhara Rao Kancherla, Neelima Guntupalli, Simhadri Mallikarjuna Rao and Archana Kalidindi. “Adaptive Ensemble Models for Robust Intrusion Detection in Cloud Environment on Imbalanced Dataset”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.8 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160829

@article{K2025,
title = {Adaptive Ensemble Models for Robust Intrusion Detection in Cloud Environment on Imbalanced Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160829},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160829},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {8},
author = {Swarnalatha K and Nirmalajyothi Narisetty and Gangadhara Rao Kancherla and Neelima Guntupalli and Simhadri Mallikarjuna Rao and Archana Kalidindi}
}