What does this research mean for the field?

The HybridML-CyberShield framework, which combines CNN-BiLSTM deep learning networks with ensemble machine learning classifiers and explainable AI models, achieves up to 98.4% accuracy and provides high interpretability for proactive intrusion detection in enterprise and IoT networks. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate HybridML-CyberShield, a hybrid machine learning framework designed for proactive, scalable, and explainable intrusion detection in enterprise and IoT networks.

August 23, 2026Discover ComputingOpen Access

HybridML CyberShield for explainable proactive intrusion detection in enterprise and IoT networks

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Authors

RSRamesh N. S. V. S. C. Sripada ABA. Durga Bhavani KMKiran B. Malagi

Discussion

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Overview

Computational study demonstrates high accuracy and explainability in network intrusion detection, suggesting improved threat mitigation in IoT environments.

Key Points

To develop and evaluate HybridML-CyberShield, a hybrid machine learning framework designed for proactive, scalable, and explainable intrusion detection in enterprise and IoT networks.
Integrated CNN–BiLSTM deep learning architectures for spatiotemporal traffic representation with ensemble classifiers including Random Forest, Support Vector Machine, and Gradient Boosting.
Incorporated a Proactive Threat Scoring Mechanism to prioritize threats by probability, severity, and confidence, alongside SHAP and LIME for global and local model interpretability.
Evaluated system performance on standard benchmark cybersecurity datasets, including CICIDS2017.
Achieved up to 98.4% classification accuracy on the CICIDS2017 benchmark dataset.
Demonstrated strong F1-scores, high AUC-ROC values, and reduced false-positive alert rates compared to traditional methods.
Enabled near real-time, interpretable threat detection suitable for scalable enterprise and IoT monitoring environments.

Cite This Study

Sripada et al. (2026) studied this question.

synapsesocial.com/papers/6a8aae207677a34114446de7 https://doi.org/10.1007/s10791-026-10455-9

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August 23, 2026Discover ComputingOpen Access

HybridML CyberShield for explainable proactive intrusion detection in enterprise and IoT networks

View Full Paper

Ask AI

Bookmark

Authors

RSRamesh N. S. V. S. C. Sripada ABA. Durga Bhavani KMKiran B. Malagi

Discussion

Member takes

Overview

Computational study demonstrates high accuracy and explainability in network intrusion detection, suggesting improved threat mitigation in IoT environments.

Key Points

To develop and evaluate HybridML-CyberShield, a hybrid machine learning framework designed for proactive, scalable, and explainable intrusion detection in enterprise and IoT networks.
Integrated CNN–BiLSTM deep learning architectures for spatiotemporal traffic representation with ensemble classifiers including Random Forest, Support Vector Machine, and Gradient Boosting.
Incorporated a Proactive Threat Scoring Mechanism to prioritize threats by probability, severity, and confidence, alongside SHAP and LIME for global and local model interpretability.
Evaluated system performance on standard benchmark cybersecurity datasets, including CICIDS2017.
Achieved up to 98.4% classification accuracy on the CICIDS2017 benchmark dataset.
Demonstrated strong F1-scores, high AUC-ROC values, and reduced false-positive alert rates compared to traditional methods.
Enabled near real-time, interpretable threat detection suitable for scalable enterprise and IoT monitoring environments.

Cite This Study

Sripada et al. (2026) studied this question.

synapsesocial.com/papers/6a8aae207677a34114446de7 https://doi.org/10.1007/s10791-026-10455-9

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