Security and Privacy in Networks and Multimedia, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2195

Special Issue Editors

Department of Computer and Control Engineering, Rzeszow University of Technology, Powstancow Warszawy 12, Rzeszów, Poland
Interests: computer engineering; formal modeling and testing; distributed systems; cluster computing; systems modeling; network monitoring; network security; interactive systems; Web services; Internet systems architectures
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Guest Editor
Department of Computer and Control Engineering, Rzeszow University of Technology, Powstancow Warszawy 12, Rzeszów, Poland
Interests: embedded systems; industrial control systems; cryptography; colored Petri nets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first edition of this Special Issue (https://www.mdpi.com/journal/electronics/special_issues/Q5660YL8P2), in the second edition, we continue to encourage all those interested in the topic to promote their vision and ideas.

This Special Issue intends to collect high-quality research and review articles concerning privacy protection and security in networks and multimedia processing. We also welcome academic articles that cover security and privacy policies related to IoT and multimedia services.

The topics of interest include, but are not limited to, the following:

  • New technological developments;
  • Security architectures and platforms;
  • Intrusion detection;
  • Anonymization techniques;
  • Access control;
  • AI security;
  • Multimedia services;
  • Processing of encrypted multimedia;
  • Privacy-preserving data mining;
  • Secure multimedia distribution in cloud computing;
  • Cryptographic protocols;
  • Security and privacy in online social networks;
  • Security and privacy issues in smart homes;
  • Empirical research and case studies;
  • Web application security.

Dr. Tomasz Rak
Dr. Dariusz Rzońca
Guest Editors

Manuscript Submission Information

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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. Electronics 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

  • security
  • privacy
  • access control
  • cloud computing security
  • data mining security
  • security algorithms
  • data security for mobile internet and social networks
  • games for cybersecurity training and awareness
  • multimedia service
  • enhancement in web infrastructure
  • smart homes

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Related Special Issue

Published Papers (3 papers)

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Research

18 pages, 683 KiB  
Article
InMesh: A Zero-Configuration Agentless Endpoint Detection and Response System
by Angel Kodituwakku and Jens Gregor
Electronics 2025, 14(7), 1292; https://doi.org/10.3390/electronics14071292 - 25 Mar 2025
Viewed by 245
Abstract
Endpoint Detection and Response (EDR) systems play a crucial role in continuously monitoring endpoint activities to detect, analyze, and respond to cybersecurity threats in real time. Traditional agent-based EDR systems rely on software agents installed on endpoints for data collection, which can be [...] Read more.
Endpoint Detection and Response (EDR) systems play a crucial role in continuously monitoring endpoint activities to detect, analyze, and respond to cybersecurity threats in real time. Traditional agent-based EDR systems rely on software agents installed on endpoints for data collection, which can be impractical due to the large number of devices, their mobility, and privacy concerns. In contrast, agentless EDR systems aim to overcome these limitations by remotely collecting network and host data, but they face challenges in precise data attribution because of the transient nature of network addresses. Achieving a fully zero-configuration agentless EDR system remains a significant challenge. This paper introduces InMesh, an innovative system that can identify and monitor endpoints without relying on network addressing or software agents. The effectiveness of the approach is demonstrated using real-world data. Full article
(This article belongs to the Special Issue Security and Privacy in Networks and Multimedia, 2nd Edition)
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18 pages, 3118 KiB  
Article
A Methodology for Online Situational Awareness Provision in a Business Entity
by Marek Amanowicz
Electronics 2025, 14(3), 470; https://doi.org/10.3390/electronics14030470 - 24 Jan 2025
Viewed by 582
Abstract
This paper presents a practical approach to building situational awareness at a critical infrastructure entity and its implementation in supporting security management. It outlines the main factors for achieving a high level of situational awareness and ensuring the safety and continuity of business [...] Read more.
This paper presents a practical approach to building situational awareness at a critical infrastructure entity and its implementation in supporting security management. It outlines the main factors for achieving a high level of situational awareness and ensuring the safety and continuity of business objectives. Among other things, situational awareness requires precise identification and understanding of multilateral dependencies of infrastructure, services, and processes executed by the entity and services provided by external businesses, resulting from the strong interrelationship of the critical infrastructure sectors and the sharing of resources. Accordingly, this paper presents a cyberspace modeling methodology that supports an in-depth analysis of the causes and consequences of threat proliferation in a complex infrastructure–business environment and its implementation in a Situational Awareness Management System (SAMS). An emphasis is placed on threat propagation analysis and dynamic risk assessment mechanisms and how they are used to identify and take preemptive actions protecting or limiting the scope of the threats’ propagation. The paper concludes with insights from a pilot implementation of the system prototype and directions for further work. Full article
(This article belongs to the Special Issue Security and Privacy in Networks and Multimedia, 2nd Edition)
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23 pages, 4260 KiB  
Article
Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting
by Attia Shabbir, Habib Ullah Manzoor, Muhmmand Naisr Manzoor, Sajjad Hussain and Ahmed Zoha
Electronics 2024, 13(23), 4803; https://doi.org/10.3390/electronics13234803 - 5 Dec 2024
Viewed by 929
Abstract
This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data [...] Read more.
This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data poisoning, which can impair model performance. We compare Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL), using line, ring and bus topologies, under adversarial conditions. Employing a three-layer Artificial Neural Network (ANN) with substation-level datasets (APEhourly,PJMEhourly, and COMEDhourly), we evaluate the system’s resilience in the absence of anomaly detection. Results indicate that DFL significantly outperforms CFL in attack resistance, achieving Mean Absolute Percentage Errors (MAPEs) of 0.48%, 4.29% and 0.702% across datasets, compared to the CFL MAPEs of 6.07%, 18.49% and 10.19%. This demonstrates the potential of DFL as a resilient, secure solution for load forecasting in smart grids, minimizing dependence on anomaly detection to maintain data integrity. Full article
(This article belongs to the Special Issue Security and Privacy in Networks and Multimedia, 2nd Edition)
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