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Feature Papers in the Section of Network Security and Privacy
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Feature Papers in the Section of Network Security and Privacy

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Network Security and Privacy".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 7231

Special Issue Editors

Prof. Dr. Jordi Mongay Batalla

E-Mail Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: internet architecture and applications; multimedia; IoT; smart city; smart home; UAV; software defined networking; network function virtualization; 5G/6G
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pascal Lorenz

E-Mail Website
Guest Editor
Department of Network and Telecommunication, University of Haute Alsace, 68008 Colmar, France
Interests: QoS; wireless networks; high-speed networks
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Chatzigiannakis

E-Mail Website
Guest Editor
Department of Computer, Control & Informatics Engineering, Sapienza University of Rome, 00185 Rome, Italy
Interests: distributed computing; algorithmic engineering; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue spotlights high-quality feature papers that provide a broad overview of the field of network security and privacy.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Security architectures of telecommunication systems;
  • Security in open telco infrastructures;
  • Key agreement protocols and mechanisms;
  • Cryptography for advanced secure protocols;
  • The security of supporting ICT infrastructures;
  • The security and privacy of ICT generic products;
  • The confidentiality, integrity, and availability of information;
  • Quantum computing risks and challenges;
  • Security risk assessment and security assurance;
  • Regulation progress on security assessment.

Dr. Jordi Mongay Batalla
Prof. Dr. Pascal Lorenz
Dr. Ioannis Chatzigiannakis
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Sensor and Actuator Networks 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 2000 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 and privacy
  • key agreement protocols
  • cryptography
  • security of ICT infrastructures
  • confidentiality
  • integrity and availability

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (3 papers)

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Research

16 pages, 2729 KB  
Article
Introducing the Slowloris E-DoS Attack: A Threat Arising from Vulnerabilities in the FTP and SSH Protocols
by Nikola Gavric, Guru Bhandari and Andrii Shalaginov
J. Sens. Actuator Netw. 2026, 15(2), 34; https://doi.org/10.3390/jsan15020034 - 17 Apr 2026
Viewed by 649
Abstract
Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols [...] Read more.
Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols induces unnecessary CPU utilization. In this study, we repurpose Slowloris as an energy-oriented (E-DoS) attack that exploits pre-authentication statefulness of the most prevalent remote access protocols, the Secure Shell Protocol (SSH) and File Transfer Protocol (FTP). We employ a Raspberry Pi-based experimental setup with different software implementations of the mentioned protocols to validate our hypothesis. Our experiments confirm the susceptibility of SSH and FTP to Slowloris E-DoS attacks, and we quantify the consequential impact on power consumption. We find that the Slowloris E-DoS attack exhibits an asymmetrical nature, causing a disproportionate computational demand on victim systems compared to the resources invested by the attacker. The results of this study indicate that battery-powered single-board computers (SBCs) are critically affected by these attacks due to their limited power availability. This research demonstrates the importance of understanding and mitigating Slowloris E-DoS vulnerabilities in the SSH and FTP protocols, offering valuable insights for enhancing security measures. Our findings show that millions of SBCs worldwide may be at risk and highlight a deeper structural weakness: the stateful design of widely deployed protocols can turn service availability into an energy liability. This systemic risk extends beyond SSH and FTP, with implications for IoT devices and backends that depend on stateful communication protocols. Full article
(This article belongs to the Special Issue Feature Papers in the Section of Network Security and Privacy)
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35 pages, 3162 KB  
Article
An LLM-Based Agentic Network Traffic Incident-Report Approach Towards Explainable-AI Network Defense
by Chia-Hong Chou, Arjun Sudheer and Younghee Park
J. Sens. Actuator Netw. 2026, 15(2), 32; https://doi.org/10.3390/jsan15020032 - 7 Apr 2026
Viewed by 842
Abstract
Traditional intrusion detection systems for IoT networks achieve high classification accuracy but lack interpretability and actionable incident-response capabilities, limiting their operational value in security-critical environments. This paper presents a graph-based multi-agent framework that integrates ensemble machine learning with Large Language Model (LLM)-powered incident [...] Read more.
Traditional intrusion detection systems for IoT networks achieve high classification accuracy but lack interpretability and actionable incident-response capabilities, limiting their operational value in security-critical environments. This paper presents a graph-based multi-agent framework that integrates ensemble machine learning with Large Language Model (LLM)-powered incident report generation via Retrieval-Augmented Generation (RAG). The system employs a three-phase architecture: (1) a lightweight Random Forest binary pre-detection, achieving 99.49% accuracy with a 6 MB model size for edge deployment; (2) ensemble classification combining Multi-Layer Perceptron, Random Forest, and XGBoost with soft voting and SHAP-based feature attribution for explainability; and (3) a ReAct-based summary agent that synthesizes classification results with external threat intelligence from Web search and scholarly databases to generate evidence-grounded incident reports. To address the challenge of evaluating non-deterministic LLM outputs, we introduce custom RAG evaluation metrics—faithfulness and groundedness implemented via the LLM-as-Judge framework. Experimental validation on the ACI IoT Network Dataset 2023 demonstrates ensemble accuracy exceeding 99.8% across 11 attack classes; perfect groundedness scores (1.0), indicating all generated claims derive from the retrieved context; and moderate faithfulness (0.64), reflecting appropriate analytical synthesis. The ensemble approach mitigates individual model weaknesses, improving the UDP Flood F1 score from 48% (MLP alone) to 95% through soft voting. This work bridges the gap between high-accuracy detection and trustworthy, actionable security analysis for automated incident-response systems. Full article
(This article belongs to the Special Issue Feature Papers in the Section of Network Security and Privacy)
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17 pages, 4996 KB  
Article
Safeguarding Personal Identifiable Information (PII) after Smartphone Pairing with a Connected Vehicle
by Jason Carlton and Hafiz Malik
J. Sens. Actuator Netw. 2024, 13(5), 63; https://doi.org/10.3390/jsan13050063 - 6 Oct 2024
Cited by 5 | Viewed by 4561
Abstract
The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system [...] Read more.
The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system (MAS)-based hierarchical architectures and privacy-preserving strategies for mixed-autonomy platoon control, underscore the increasing complexity of privacy management within these environments. Rental cars with infotainment systems pose substantial challenges, as renters often fail to delete their data, leaving it accessible to subsequent renters. This study investigates the risks associated with PII in connected vehicles and emphasizes the necessity of automated solutions to ensure data privacy. We introduce the Vehicle Inactive Profile Remover (VIPR), an innovative automated solution designed to identify and delete PII left on infotainment systems. The efficacy of VIPR is evaluated through surveys, hands-on experiments with rental vehicles, and a controlled laboratory environment. VIPR achieved a 99.5% success rate in removing user profiles, with an average deletion time of 4.8 s or less, demonstrating its effectiveness in mitigating privacy risks. This solution highlights VIPR as a critical tool for enhancing privacy in connected vehicle environments, promoting a safer, more responsible use of connected vehicle technology in society. Full article
(This article belongs to the Special Issue Feature Papers in the Section of Network Security and Privacy)
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