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Autonomous Cyber-Defense in IIoT using Predictive Deep Learning and Gradient Boosting
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 115 |
| Year of Publication: 2026 |
| Authors: Joseph Sujoy Pulivarthi, Suman Jana, Sai Sudheer Tadi, Lokeshwar Sai Gummidi, Veeranjaneyulu Rajamahendravarapu, P. Abdul Subhahanalla |
10.5120/ijcad609e4b0137a
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Joseph Sujoy Pulivarthi, Suman Jana, Sai Sudheer Tadi, Lokeshwar Sai Gummidi, Veeranjaneyulu Rajamahendravarapu, P. Abdul Subhahanalla . Autonomous Cyber-Defense in IIoT using Predictive Deep Learning and Gradient Boosting. International Journal of Computer Applications. 187, 115 ( Jun 2026), 6-13. DOI=10.5120/ijcad609e4b0137a
Abstract
Industrial Internet of Things (IIoT) environments are increasingly vulnerable to sophisticated cyber threats due to their distributed and heterogeneous nature. Traditional intrusion detection systems struggle to achieve real-time detection while maintaining interpretability. This paper presents a hybrid, real-time, and explainable cyber-defense framework for IIoT environments, integrating ensemble machine learning with explainable artificial intelligence (XAI). The proposed system employs a multi-stage hybrid detection pipeline integrating Isolation Forest for anomaly detection, XGBoost for attack classification, and SHAP-based explainability for interpretable threat analysis. Additionally, SHAP (SHapley Additive exPlanations) is utilized to provide feature-level interpretability. The system is evaluated on the CICIDS-2017 dataset, achieving high accuracy, reduced false positives, and an average inference latency of approximately 12 ms. Experimental results demonstrate that the proposed approach outperforms traditional models in both detection performance and explainability, making it suitable for real-time IIoT security applications. The proposed framework integrates anomaly detection, gradient boosting-based attack classification, explainable artificial intelligence (XAI), and real-time monitoring into a unified cyber-defense architecture suitable for Industrial Internet of Things environments.
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