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Applied Sciences
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Advances in Computer Networks and Software-Defined Networks
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Advances in Computer Networks and Software-Defined Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 1281

Editors

Dr. Jerzy Domzal

E-Mail Website
Guest Editor
Institute of Telecommunications, AGH University of Krakow, Krakow, Poland
Interests: computer networks; computer networking; telecommunications engineering; information theory; QoS; routing
Dr. Edyta Biernacka

E-Mail Website
Guest Editor
Institute of Telecommunications, AGH University of Krakow, Krakow, Poland
Interests: computer networks; computer networking; telecommunications engineering; information theory; optical networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue.

The concept of Software-Defined Networks (SDNs) is widely known. It involves separating control panels from data to enable efficient transmission. Recent years have seen significant developments in internet technologies. Most of these are related to machine-learning algorithms, which can significantly improve the operation of computer networks. Machine-learning solutions are being implemented alongside the concept of SDNs.

This Special Issue invites submissions of articles covering all aspects of SDNs, including new controllers, security, traffic engineering based on machine-learning algorithms and more. Both theoretical and experimental papers, as well as comprehensive review and overview articles, are welcome.

Dr. Jerzy Domzal
Dr. Edyta Biernacka
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • software-defined networks
  • machine-learning
  • traffic engineering
  • cross-layer networks
  • SDN controllers

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Published Papers (3 papers)

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Research

25 pages, 882 KB  
Article
Impact of Network Topology on Machine Learning-Based DDoS and Anomaly Detection in Software-Defined Networks
by Łukasz Bakuła and Andrzej Jasinski
Appl. Sci. 2026, 16(12), 6204; https://doi.org/10.3390/app16126204 - 19 Jun 2026
Viewed by 218
Abstract
The development of Software-Defined Networks (SDNs) introduces new challenges in network security, particularly in detecting Distributed Denial of Service (DDoS) attacks and network anomalies. Due to the centralized architecture of SDN, traditional detection methods are often insufficient in dynamic environments. Therefore, machine learning [...] Read more.
The development of Software-Defined Networks (SDNs) introduces new challenges in network security, particularly in detecting Distributed Denial of Service (DDoS) attacks and network anomalies. Due to the centralized architecture of SDN, traditional detection methods are often insufficient in dynamic environments. Therefore, machine learning techniques are increasingly applied to improve detection effectiveness. This paper analyzes the impact of network topology on the performance of machine learning-based detection methods in SDN environments. A controlled experimental setup based on the RYU controller and OpenFlow 1.3 was implemented using Mininet. Two network topologies (linear and hierarchical) were evaluated under multiple attack scenarios, including TCP SYN flood and TCP/UDP port scanning. Two supervised learning models, Random Forest (RF) and K-Nearest Neighbors (KNN), were implemented and compared using standard evaluation metrics: accuracy, precision, recall, F1-score, and detection time. The results show that Random Forest significantly outperforms KNN, achieving up to 100% accuracy and detection times as low as 4.24 s, while KNN exhibits lower stability and reduced recall in anomaly detection scenarios. The study demonstrates that network topology has a measurable impact on both detection performance and latency. The observed effects varied across attack scenarios and machine learning models. Hierarchical topology generally improved detection sensitivity in DDoS scenarios, while linear topology often enabled lower detection latency during selected anomaly detection experiments. The results indicate that both machine learning model selection and network topology should be jointly considered when designing intrusion detection systems for SDN environments. These findings contribute to improving the effectiveness and responsiveness of security mechanisms in modern programmable networks. Full article
(This article belongs to the Special Issue Advances in Computer Networks and Software-Defined Networks)
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24 pages, 1054 KB  
Article
Hybrid Intrusion Detection System for Software-Defined Networks
by Aleksandra Łapczuk, Jerzy Domżał, Edyta Biernacka and Robert Wójcik
Appl. Sci. 2026, 16(12), 6122; https://doi.org/10.3390/app16126122 - 17 Jun 2026
Viewed by 216
Abstract
Software-Defined Networking, as a relatively recent networking paradigm, offers centralized infrastructure management, flexibility and high programmability. However, it also creates particular security risks due to being exposed to external threats. To address these challenges, numerous methods have been developed and applied over the [...] Read more.
Software-Defined Networking, as a relatively recent networking paradigm, offers centralized infrastructure management, flexibility and high programmability. However, it also creates particular security risks due to being exposed to external threats. To address these challenges, numerous methods have been developed and applied over the past few years. This study proposes a hybrid Intrusion Detection System that combines signature-based analysis with deep learning-based anomaly detection. In this architecture, a signature module quickly filters known attack patterns, while remaining traffic is analyzed by an autoencoder and a supervised deep neural network classifier. The final decision is based on rule-based prioritization of the outputs from both models, improving the reliability and robustness of detection. Full article
(This article belongs to the Special Issue Advances in Computer Networks and Software-Defined Networks)
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23 pages, 1737 KB  
Article
Log-Driven Proximal Policy Optimization for Adaptive Traffic Control in Software-Defined Networks
by Abzal E. Kyzyrkanov, Yedil S. Nurakhov, Zhenis Otarbay and Danil V. Lebedev
Appl. Sci. 2026, 16(9), 4424; https://doi.org/10.3390/app16094424 - 1 May 2026
Viewed by 429
Abstract
Software-Defined Networking (SDN) enables centralised and programmable traffic control, but adaptive optimization in operational networks remains challenging when safe online exploration is limited and only historical controller traces are available. This study proposes a log-driven Proximal Policy Optimisation (PPO) framework for adaptive SDN [...] Read more.
Software-Defined Networking (SDN) enables centralised and programmable traffic control, but adaptive optimization in operational networks remains challenging when safe online exploration is limited and only historical controller traces are available. This study proposes a log-driven Proximal Policy Optimisation (PPO) framework for adaptive SDN traffic control that learns directly from recorded state–action–reward transitions. The method uses a replay-based pseudo-environment constructed from controller logs. It combines clipped PPO updates with action-consistency regularisation and running state normalisation to improve stability under logged-data constraints. The empirical evaluation shows that the learned model reconstructs the dominant response pattern observed in the traces, preserves a positive relationship between the principal control-related predictor and the response, and reveals a non-uniform interaction structure across telemetry features. The framework also differentiates systematically across operating conditions and experimental groups, with category means ranging from 0.78 to 1.24 and group medians ranging from −0.12 to 1.12, while receiver operating characteristic analysis yields an area under the curve of 0.714. The practical network evaluation further shows that the PPO-controlled setting improves overall throughput, packet loss, jitter, and flow-completion success relative to the baseline controller. These results indicate that log-driven, stability-constrained PPO can provide a stable and informative basis for adaptive SDN traffic control when policy learning must rely on historical controller data rather than unrestricted live-network experimentation. Full article
(This article belongs to the Special Issue Advances in Computer Networks and Software-Defined Networks)
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