| 1 |
Author(s):
Ikhioya Emmanuel, Ajaegbu C. (Prof.).
Page No : 1-14
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Load Balancing for Virtual Machines in Heterogeneous Data Center Networks Using Software Defined Networking Integrated with Multi Criteria Decision Making: A Systematic Review.
Abstract
Efficient load balancing is vital in heterogeneous data center networks to ensure optimal resource use and service performance. Traditional methods struggle with dynamic VM workloads and diverse resource demands. This systematic review explores the integration of Software-Defined Networking (SDN) and Multi-Criteria Decision Making (MCDM) as a solution. SDN offers centralized, programmable control, while MCDM enables intelligent decision-making across multiple performance metrics. Reviewing recent literature, this study highlights key techniques—such as the Weighted Sum Model (WSM)—and identifies gaps in real-time adaptability and metric integration. The review serves as a foundation for developing scalable, QoS-aware load balancing systems in cloud environments.
| 2 |
Author(s):
Emmanuel Ikhioya.
Page No : 15-31
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Security Threat Mitigation in SDN.
Abstract
Software-Defined Networking (SDN) has transformed network management by decoupling the control and data planes, enabling centralized control and programmability. While SDN enhances flexibility and scalability, its centralized architecture introduces critical security challenges, including Distributed Denial of Service (DDoS) attacks, API exploits, and controller compromises. This study provides a comprehensive review of SDN security vulnerabilities and evaluates mitigation techniques such as authentication protocols, anomaly detection systems, resilient architectures, and secure communication protocols. The findings highlight the importance of multi-layered defense strategies to safeguard SDN environments and address evolving cyber threats. Gaps in scalability, real-time adaptation, and integration with emerging technologies are also identified, paving the way for future research.
| 3 |
Author(s):
Emmanuel Ikhioya, Adeyemi John.
Page No : 32-47
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Network Performance Optimization with Intent‑Based Networking: A Review.
Abstract
Intent-Based Networking (IBN) is transforming network management by shifting from manual, device-oriented configurations to high-level, intent-driven automation. In an IBN system, network operators express high-level objectives that are automatically translated into detailed network policies and configurations through artificial intelligence (AI), natural language processing (NLP), and closed-loop control mechanisms. This paper reviews recent advances in IBN, with a focus on its architecture, automation techniques, security frameworks, and AI-driven policy generation. Moreover, we discuss intent negotiation frameworks and voice-enabled interfaces for industrial automation. By integrating these technologies with Software-Defined Networking (SDN) and Network Function Virtualization (NFV), IBN optimizes network performance by enhancing resource utilization, reducing latency, and improving overall reliability. This paper also outlines the challenges and future research directions necessary for the deployment of IBN in next-generation networks.
| 4 |
Author(s):
Ibrahim Abdul Sa'ad, Collins Nnalue Udanor, Modesta E. Ezema, Caleb Markus, Mathew Akwu Adaji.
Page No : 48-63
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A Systematic Review of Federated Deep Learning Models for Intrusion Detection in Distributed Satellite Data Centres.
Abstract
This systematic review evaluates existing federated and deep learning intrusion detection approaches with a focus on their suitability for distributed satellite and critical infrastructure environments. Following PRISMA procedures, 13,213 records were identified, 11,265 duplicates removed, 1,948 screened, and 10 studies met full eligibility criteria. Across these studies, federated learning models achieved competitive detection outcomes, with reported accuracy ranging from eighty-eight percent to ninety-seven percent and F1 scores between eighty-five percent and ninety-six percent, often differing from centralised models by less than three percent. Communication efficiency improved substantially, with several studies demonstrating reductions of thirty to sixty percent in update bandwidth due to parameter rather than data transmission. Privacy preservation scored consistently high across all federated implementations, while centralised systems showed significant exposure risk. Weaknesses emerged in handling non-independent data, where performance dropped by up to ten percent in some studies, and in susceptibility to gradient poisoning, which degraded accuracy by seven percent in controlled experiments. Mathematical formulations remained underdeveloped, with limited convergence proofs and insufficient modelling of secure aggregation. The findings indicate that federated deep learning is methodologically superior for satellite data centres but requires realistic satellite traffic datasets, hybrid optimisation such as blockchain-assisted aggregation, and improved mathematical modelling to ensure robustness.
| 5 |
Author(s):
Ibrahim Abdul Sa'ad, Collins Nnalue Udanor, Modesta E. Ezema, Caleb Markus, Mathew Akwu Adaji.
Page No : 64-75
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Deep Learning and Explainable AI Models for Intrusion Detection in Space-Ground Communication Networks: A Review.
Abstract
This review critically evaluates the suitability of deep learning and explainable artificial intelligence approaches for intrusion detection in satellite ground-station environments, addressing the escalating cybersecurity risks facing the National Space Research and Development Agency (NASRDA) and broader space communication networks. Using a systematic narrative review across IEEE Xplore, ACM, Scopus and arXiv, the analysis compares CNN, LSTM, GRU, autoencoder and transformer-based IDS models, revealing that while reported accuracies frequently exceed 92% on benchmark datasets, performance declines by 20% to 35% under domain shift, demonstrating poor transferability to space–ground telemetry. XAI methods such as SHAP, LIME and Integrated Gradients appear in more than 80% of reviewed studies, yet empirical results show a 30% to 60% increase in inference latency, raising concerns about operational feasibility in real-time satellite control systems. A mathematical hybrid model combining CNN, LSTM and transformer components with a structured anomaly-scoring function and explanation regulariser is formulated to address these limitations. Findings indicate that multi-model fusion enhances anomaly sensitivity, domain-specific feature engineering improves robustness, and integrated XAI pathways strengthen analyst trust while exposing computational bottlenecks. The proposed conceptual architecture for NASRDA advances the field by aligning detection workflows, interpretability mechanisms and feedback loops with the constraints of aerospace communication systems. The review concludes by identifying key research priorities, including the development of satellite-specific datasets, real-traffic validation of hybrid IDS models, and deployment of low-latency XAI dashboards for operational security.
