Ensemble Based Intrusion Detection in Heterogeneous Networks: A Machine Learning Framework with Zero Trust Integration
Abstract
Contemporary enterprise networks face an escalating proliferation of sophisticated cyber threats that evade traditional signature-based detection systems. This paper proposes the Adaptive Ensemble Threat Detection (AETD) framework a novel machine learning architecture integrating Random Forest, Bidirectional Long Short-Term Memory (BiLSTM), Autoencoder based anomaly detection, and XGBoost within a dynamically weighted ensemble voting mechanism. AETD addresses three critical limitations of existing intrusion detection systems: (1) inadequate generalization to zero day and novel attack variants; (2) class imbalance in training data leading to high false positive rates; and (3) the absence of real time adaptive weight updating in response to evolving threat patterns. The framework is further contextualized within a Zero Trust Architecture (ZTA) paradigm, enabling identity aware, continuous verification-based access control that complements network layer detection. Extensive experimental evaluation on the CICIDS2018 benchmark dataset (16.2 million network flow records, 14 attack classes) demonstrates that AETD achieves 99.43% classification accuracy and 0.57% false positive rate, outperforming all individual constituent models and classical baselines by statistically significant margins (p < 0.001). Ablation studies confirm the contribution of each ensemble component. The proposed framework is positioned within a comprehensive Security Operations Centre (SOC) integration model encompassing SIEM correlation, SOAR automated response, and MITRE ATT&CK aligned detection coverage. Results demonstrate a 38% improvement in mean time to detection (MTTD) compared to rule only SIEM deployments.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 6 No. 1 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
10827-10837 |
Published |
February 16, 2023 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Pavan Navandar (2023). Ensemble Based Intrusion Detection in Heterogeneous Networks: A Machine Learning Framework with Zero Trust Integration. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 6 No. 1 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 10827-10837. https://doi.org/10.15662/IJAESIT.2023.0601004 |
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