Privacy and Authentication for Communication Networks

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2858

Special Issue Editors


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Guest Editor
Program of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: information and network security; wireless sensor networks; mobile computing security; Internet of Things security; cloud computing security; blockchain security and its application; RFID security and its application; telemedicine information system security; security protocols for ad hoc networks; information retrieval and dictionary search
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Guest Editor
School of Engineering, University of Mount Union, Alliance, OH 44601-3993, USA
Interests: ML/federated learning in wireless systems; heterogeneous networks; massive MIMO; reconfigurable intelligent surface-assisted networks; mmWave communication networks; energy harvesting; full-duplex communications; cognitive radio; small cell; non-orthogonal multiple access (NOMA); physical layer security; UAV networks; visible light communication; IoT system
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Bachelor's Program of Artificial Intelligence and Information Security, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: information security; cryptography; blockchain; smart communications; healthcare communication security; smart grid communication
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
2. Department of Electrical Engineering and Information Technology, Ruhr University, 44801 Bochum, Germany
Interests: 6G wireless communication systems; cell-free massive MIMO systems; energy-efficient wireless systems; channel modeling; artificial intelligence; wireless security systems; chaotic communication; sustainable communication; telecommunication networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Privacy and authentication are crucial aspects of modern communication networks, which are becoming increasingly interconnected and ubiquitous. With the proliferation of smart devices, IoT applications, and cloud-based services, ensuring privacy and authentication is of paramount importance. In recent years, many research works have been proposed to address the multiple security and privacy issues in communication networks. Unfortunately, the existing works still have drawbacks and limitations in terms of functionality or performance when considering the security of the designs. This Special Issue aims to explore the latest advancements, challenges, and solutions in the fields of privacy and authentication for communication networks. We invite original research contributions, reviews, and case studies on, but not limited to, the following topics:

  1. Privacy-preserving protocols for communication networks;
  2. Authentication mechanisms for 5G and beyond;
  3. Privacy and security issues in IoT networks and edge computing;
  4. Privacy-aware data sharing in cloud-based services;
  5. Threat modeling and risk assessment for communication networks;
  6. Biometric-based authentication and identification methods;
  7. Machine learning approaches to enhance privacy and authentication;
  8. Blockchain-based solutions for secure communication;
  9. Privacy-enhancing technologies for social networks and messaging apps;
  10. Usability and user-centric design of authentication systems;
  11. Cryptographic algorithms and systems for secure communication.

Prof. Dr. Cheng-Chi Lee
Prof. Dr. Chun-Ta Li
Dr. Dinh-Thuan Do
Dr. Tuan-Vinh Le
Dr. Agbotiname Lucky Imoize
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. Future Internet 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

  • privacy
  • authentication
  • cryptography
  • IoT
  • cloud systems
  • edge computing
  • machine learning
  • blockchain
  • 5G/6G
  • secure sharing

Published Papers (1 paper)

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Research

22 pages, 864 KiB  
Article
Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
by James Msughter Adeke, Guangjie Liu, Junjie Zhao, Nannan Wu and Hafsat Muhammad Bashir
Future Internet 2023, 15(12), 405; https://doi.org/10.3390/fi15120405 - 16 Dec 2023
Viewed by 2184
Abstract
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but [...] Read more.
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs. Full article
(This article belongs to the Special Issue Privacy and Authentication for Communication Networks)
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