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An Adaptive Hybrid Machine Learning Framework for Early Detection of Network Intrusions in Cloud Computing Environments
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 107 |
| Year of Publication: 2026 |
| Authors: Kruti D. Desai, Roshani S. Patel |
10.5120/ijcabc88bac48614
|
Kruti D. Desai, Roshani S. Patel . An Adaptive Hybrid Machine Learning Framework for Early Detection of Network Intrusions in Cloud Computing Environments. International Journal of Computer Applications. 187, 107 ( May 2026), 36-41. DOI=10.5120/ijcabc88bac48614
Abstract
The increasing reliance on cloud computing has introduced significant security challenges, particularly in detecting sophisticated and evolving cyber-attacks. Traditional intrusion detection systems (IDS) are often limited by their dependence on predefined signatures and lack the ability to identify unknown threats in dynamic environments. To address these limitations, this paper proposes an adaptive hybrid machine learning framework for early detection of network intrusions in cloud computing environments. The proposed approach integrates an Autoencoder-based anomaly detection model with a Random Forest classifier to effectively identify both known and unknown attack patterns. The Autoencoder learns normal network behaviour and detects deviations, while the Random Forest model classifies the detected anomalies into specific attack categories. In addition, an adaptive learning mechanism is incorporated to continuously update the model using new network data, ensuring improved performance over time. The system is evaluated using the CICIDS2017 dataset, and its performance is measured using standard metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed model achieves high detection accuracy with a low false positive rate compared to traditional methods. The findings suggest that combining supervised and unsupervised learning techniques with adaptive capabilities can significantly enhance intrusion detection in cloud environments. This work contributes toward developing intelligent, scalable, and efficient cybersecurity solutions for modern cloud infrastructures.
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