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Artificial Intelligence for Network Management
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Artificial Intelligence for Network Management

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 14312

Special Issue Editors

Dr. Stephen Ojo

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC 29621, USA
Interests: machine learning and artificial intelligence for wireless communication; AI and ML in biomedicine; multi-channel modeling; propagation
Dr. Agbotiname Lucky Imoize

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
Interests: 6G wireless communication systems; cell-free massive MIMO systems; energy-efficient wireless systems; propagation measurements; channel modeling; artificial intelligence; machine learning; wireless security systems; cryptography; chaotic communication; sustainable communication; blockchain technology
Special Issues, Collections and Topics in MDPI journals
Dr. Lateef Adesola Akinyemi

E-Mail Website
Guest Editor
1. Department of Computer Science, School of Computing, College of Science, Engineering and Technology, University of South Africa, Johannesburg 1709, Africa
2. Centre for Augmented Intelligence and Data Science (CAIDS), University of South Africa, Johannesburg 1709, South Africa
Interests: radio wave propagation; wireless communications; quantum electronics; microwave engineering; antennas’ electromagnetic waves and field wireless communications; numerical analysis and application digital signal processing; privacy, security and authentication

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the use of AI and machine learning in network management.  It is known that artificial intelligence (AI) is revolutionizing network management by enhancing efficiency, security and performance. AI-driven solutions enable automated network monitoring, predictive maintenance and proactive issue resolution. Machine learning algorithms analyze vast amounts of network data to detect anomalies, predict failures and optimize traffic flow. By continuously learning and adapting to network conditions, AI enhances the scalability and reliability of network infrastructure, ultimately leading to improved user experiences and operational efficiency.

This Special Issue calls for papers on AI-powered network management that supports the growing complexity of modern networks, including cloud services, IoT devices and 5G technology. It welcomes research articles that present novel theories, algorithms, systems and applications of AI in fields such as network monitoring, predictive maintenance, anomaly detection, traffic optimization, automated issue resolution, adaptive security measures, 5G network enhancement, IoT device management, cloud service integration and network scalability.

Dr. Stephen Ojo
Dr. Agbotiname Lucky Imoize
Dr. Lateef Adesola Akinyemi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent network monitoring
  • predictive maintenance
  • anomaly detection
  • traffic optimization
  • automated issue resolution
  • adaptive security measures
  • 5G network enhancement
  • IoT device management
  • cloud service integration and network scalability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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31 pages, 2736 KiB  
Article
Unseen Attack Detection in Software-Defined Networking Using a BERT-Based Large Language Model
by Mohammed N. Swileh and Shengli Zhang
AI 2025, 6(7), 154; https://doi.org/10.3390/ai6070154 - 11 Jul 2025
Viewed by 417
Abstract
Software-defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift introduces significant security challenges, as SDN’s centralized control becomes an attractive target [...] Read more.
Software-defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift introduces significant security challenges, as SDN’s centralized control becomes an attractive target for various types of attacks. While the body of current research on attack detection in SDN has yielded important results, several critical gaps remain that require further exploration. Addressing challenges in feature selection, broadening the scope beyond Distributed Denial of Service (DDoS) attacks, strengthening attack decisions based on multi-flow analysis, and building models capable of detecting unseen attacks that they have not been explicitly trained on are essential steps toward advancing security measures in SDN environments. In this paper, we introduce a novel approach that leverages Natural Language Processing (NLP) and the pre-trained Bidirectional Encoder Representations from Transformers (BERT)-base-uncased model to enhance the detection of attacks in SDN environments. Our approach transforms network flow data into a format interpretable by language models, allowing BERT-base-uncased to capture intricate patterns and relationships within network traffic. By utilizing Random Forest for feature selection, we optimize model performance and reduce computational overhead, ensuring efficient and accurate detection. Attack decisions are made based on several flows, providing stronger and more reliable detection of malicious traffic. Furthermore, our proposed method is specifically designed to detect previously unseen attacks, offering a solution for identifying threats that the model was not explicitly trained on. To rigorously evaluate our approach, we conducted experiments in two scenarios: one focused on detecting known attacks, achieving an accuracy, precision, recall, and F1-score of 99.96%, and another on detecting previously unseen attacks, where our model achieved 99.96% in all metrics, demonstrating the robustness and precision of our framework in detecting evolving threats, and reinforcing its potential to improve the security and resilience of SDN networks. Full article
(This article belongs to the Special Issue Artificial Intelligence for Network Management)
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17 pages, 6124 KiB  
Article
Machine Learning-Based Network Anomaly Detection: Design, Implementation, and Evaluation
by Pilar Schummer, Alberto del Rio, Javier Serrano, David Jimenez, Guillermo Sánchez and Álvaro Llorente
AI 2024, 5(4), 2967-2983; https://doi.org/10.3390/ai5040143 - 17 Dec 2024
Cited by 4 | Viewed by 11175
Abstract
Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network [...] Read more.
Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies. SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability. Full article
(This article belongs to the Special Issue Artificial Intelligence for Network Management)
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Review

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59 pages, 4517 KiB  
Review
Artificial Intelligence Empowering Dynamic Spectrum Access in Advanced Wireless Communications: A Comprehensive Overview
by Abiodun Gbenga-Ilori, Agbotiname Lucky Imoize, Kinzah Noor and Paul Oluwadara Adebolu-Ololade
AI 2025, 6(6), 126; https://doi.org/10.3390/ai6060126 - 13 Jun 2025
Viewed by 1454
Abstract
This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive [...] Read more.
This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance. Full article
(This article belongs to the Special Issue Artificial Intelligence for Network Management)
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