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AI Tools and Methods for Computer 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 June 2025 | Viewed by 3208

Special Issue Editors


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Guest Editor
Department of Computer Networks and Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: streaming video; video proces; modelling, networking

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Guest Editor
Department of Computer Science, Università degli Studi di Salerno, 84084 Fisciano, Italy
Interests: cryptography; information/data security; computer security; digital watermarking; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI-based tools and methods are widely used for network performance, health and security management. AI can, for example, support learning normal network behaviour and highlighting abnormal actions, or improve network traffic prediction, which results in better resource usage for network applications. AI implements one or more machine learning models, which usually incorporate neural networks, decision trees, support vector machines, regression analysis, Bayesian networks and genetic algorithms.

Although AI techniques have achieved a lot of success, there are still areas in network management with potential for improvement. Therefore, this Special Issue should present new ideas and experimental results of AI applied to network management. It includes opportunities for AI application on aspects such as analysis, modelling and visualisation applied to contemporary network infrastructures, especially mobile wireless networks, IoT, SDN and cloud and storage systems. Moreover, there is an opportunity to demonstrate the benefits of AI tools or define novel AI-based network services focused on security, performance measurement and management, traffic prediction or event detection.

Topics of interest for this Special Issue include, but are not limited to, the following:

AI Techniques for Network Management:
  • Analysis, modelling and visualization;
  • Predictive analytics and real-time analytics;
  • Data mining, statistical modelling and machine learning for management;
  • Anomaly detection and prediction;
  • Traffic prediction and classification;
  • Event and log analytics, text mining;
  • Data models for deploying AI for network management and orchestration.

Application Domains:

  • 5G, 6G and beyond;
  • Internet of Things and cyber–physical systems;
  • Software-defined networks;
  • Network services;
  • Virtualised infrastructure, clouds and edge nodes;
  • Multimedia transmission, adaptive video streaming;
  • Social networks analysis and modelling;
  • Network health and performance management
  • Security, intrusion detection, threat analysis and failure detection;
  • Platforms for network monitoring and measurements.

Dr. Arkadiusz Biernacki
Dr. Arcangelo Castiglione
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. 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

  • computer networks
  • artificial intelligence
  • machine learning
  • data mining
  • network management
  • traffic and anomaly prediction

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Published Papers (3 papers)

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Research

18 pages, 463 KiB  
Article
Interpretable Identification of Dynamic Adaptive Streaming over HTTP (DASH) Flows Based on Feature Engineering
by Arkadiusz Biernacki
Appl. Sci. 2025, 15(5), 2253; https://doi.org/10.3390/app15052253 - 20 Feb 2025
Viewed by 275
Abstract
Internet service providers allocate network resources for different network flows. Among them, video streaming requires substantial network bandwidth to provide a satisfactory user experience. The identification of video traffic is one of the tools that helps to manage and optimise network resources. However, [...] Read more.
Internet service providers allocate network resources for different network flows. Among them, video streaming requires substantial network bandwidth to provide a satisfactory user experience. The identification of video traffic is one of the tools that helps to manage and optimise network resources. However, available solutions usually focus on traffic traces from a single application and use black-box models for identification, which require labels for training. To address this issue, we proposed an unsupervised machine learning model to identify traffic generated by video applications from the three popular services, namely YouTube, Netflix, and Amazon Prime. Our methodology involves feature generation, filtering, and clustering. The clustering used the most significant features to group similar traffic patterns. We employed the following three algorithms that represent different clustering methodologies: partition-based, density-based, and probabilistic approaches. The clustering achieved precision between 0.78 and 0.93, while recall rates ranged from 0.68 to 0.84, depending on the experiment parameters, which is comparable with black-box learning models. The model presented is interpretable and scalable, which is useful for its practical application. Full article
(This article belongs to the Special Issue AI Tools and Methods for Computer Networks)
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17 pages, 22138 KiB  
Article
SQL Injection Detection Based on Lightweight Multi-Head Self-Attention
by Rui-Teng Lo, Wen-Jyi Hwang and Tsung-Ming Tai
Appl. Sci. 2025, 15(2), 571; https://doi.org/10.3390/app15020571 - 9 Jan 2025
Viewed by 758
Abstract
This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. The model features high detection accuracy, fast inference speed, and low weight size. The model is based on a novel Natural Language [...] Read more.
This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. The model features high detection accuracy, fast inference speed, and low weight size. The model is based on a novel Natural Language Processing (NLP) technique, where a tokenizer for converting SQL queries into tokens is adopted as a pre-processing stage for detection. Only SQL keywords and symbols are considered as tokens for removing noisy information from input queries. Moreover, semantic labels are assigned to tokens for highlighting malicious intentions. For the exploration of correlation among the tokens, a lightweight multi-head self-attention scheme with a position encoder is employed. Experimental results show that the proposed algorithm has high detection performance for SQL injection. In addition, compared to its lightweight NLP counterparts based on self-attention, the proposed algorithm has the lowest weight size and highest inference speed. It consumes only limited computation and storage overhead for web services. In addition, it can even be deployed in the edge devices with low computation capacity for online detection. The proposed algorithm therefore is an effective low-cost solution for SQL injection detection. Full article
(This article belongs to the Special Issue AI Tools and Methods for Computer Networks)
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24 pages, 1053 KiB  
Article
Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis
by Tomasz Walczyna, Damian Jankowski and Zbigniew Piotrowski
Appl. Sci. 2025, 15(1), 286; https://doi.org/10.3390/app15010286 - 31 Dec 2024
Viewed by 1604
Abstract
This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis [...] Read more.
This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches. Full article
(This article belongs to the Special Issue AI Tools and Methods for Computer Networks)
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