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A Holistic Architectural Framework for Digital Sovereignty in the Post-Quantum Era: Integrating Vendor Lock-In Mitigation with Quantum-Resistant Cloud Migration Strategies

Justice Opara-Martins
Reseach Article

AI-Driven Context-Aware Cybersecurity Architecture for IoT and Distributed Digital Ecosystem

by Godfrey Perfectson Oise, Evans Mintah, Oludare Sokoya, Osahon Ukpebor, Tejiri Jessa, Susan Konyeha
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Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 6
Year of Publication: 2026
Authors: Godfrey Perfectson Oise, Evans Mintah, Oludare Sokoya, Osahon Ukpebor, Tejiri Jessa, Susan Konyeha

Godfrey Perfectson Oise, Evans Mintah, Oludare Sokoya, Osahon Ukpebor, Tejiri Jessa, Susan Konyeha . AI-Driven Context-Aware Cybersecurity Architecture for IoT and Distributed Digital Ecosystem. Journal of Advanced Artificial Intelligence. 2, 6 ( May 2026), 9-17. DOI=None

@article{ None,
author = { Godfrey Perfectson Oise, Evans Mintah, Oludare Sokoya, Osahon Ukpebor, Tejiri Jessa, Susan Konyeha },
title = { AI-Driven Context-Aware Cybersecurity Architecture for IoT and Distributed Digital Ecosystem },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { May 2026 },
volume = { 2 },
number = { 6 },
month = { May },
year = { 2026 },
pages = { 9-17 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume2/number6/ai-driven-context-aware-cybersecurity-architecture-for-iot-and-distributed-digital-ecosystem/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-31T02:56:50.134303+05:30
%A Godfrey Perfectson Oise
%A Evans Mintah
%A Oludare Sokoya
%A Osahon Ukpebor
%A Tejiri Jessa
%A Susan Konyeha
%T AI-Driven Context-Aware Cybersecurity Architecture for IoT and Distributed Digital Ecosystem
%J Journal of Advanced Artificial Intelligence
%V 2
%N 6
%P 9-17
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The rapid expansion of Internet of Things (IoT) infrastructures and distributed digital ecosystems has significantly increased cybersecurity vulnerabilities, creating the need for intelligent and adaptive intrusion detection mechanisms. This study proposes a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture for AI-driven intrusion detection capable of learning both spatial feature correlations and temporal traffic dependencies. The model was evaluated using the UNSW-NB15 benchmark dataset under a binary classification setting consisting of 56,000 normal samples and 119,341 attack samples. Data preprocessing involved feature normalization, categorical encoding, and traffic feature transformation prior to model training using binary cross-entropy loss optimized with the Adam optimizer and regularized through early stopping. Experimental results demonstrate strong and balanced classification performance. The proposed model achieved an overall accuracy of 88.75%, with weighted and macro F1-scores of 0.8904 and 0.8782, respectively. The attack class achieved high precision (0.9772) and strong recall (0.8547), while normal traffic achieved a recall of 0.9574. Furthermore, the model achieved ROC-AUC and PR-AUC values of 0.98 and 0.99, confirming excellent discriminative capability and robustness under class imbalance conditions. Additional evaluation metrics, including Cohen’s Kappa (0.7584) and Matthews Correlation Coefficient (0.7714), further demonstrate strong predictive reliability and substantial agreement beyond chance.The findings confirm that hybrid spatial–temporal deep learning architectures provide an effective and scalable foundation for intrusion detection in IoT and distributed digital environments. By combining spatial feature extraction with bidirectional temporal modeling, the proposed CNN–BiLSTM framework contributes toward the development of adaptive and resilient AI-driven cybersecurity systems for next-generation digital infrastructures.

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Index Terms

Computer Science
Information Sciences

Keywords

AI-driven cybersecurity Context-aware intrusion detection IoT security Distributed digital ecosystems Deep learning architectures CNN–BiLSTM Federated learning Edge intelligence

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