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Cybersecurity and Data Privacy in the Internet of Things (IoT)—Challenges and New Horizons

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 2700

Special Issue Editor


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Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK
Interests: robotics; machine learning; internet of things; software defined networking; cyber security

Special Issue Information

Dear Colleagues,

IoT (Internet of Things) cybersecurity presents a complex-yet-interesting landscape, which raises both challenges and new horizons.

The challenges include the following:

  • Device Vulnerabilities: Many IoT devices have limited processing power and memory, which often leads to inadequate security measures.
  • Data Privacy Concerns: The inadequate protection of sensitive data can lead to privacy breaches, identity theft, or unauthorized surveillance.
  • Interoperability Issues: Different device manufacturers and communication protocols raise challenges in terms of integration and consistent security standards.
  • Physical Security: IoT devices are often deployed in physically accessible environments, exposing vulnerabilities to physical tampering or theft.
  • Scalability: As the number of connected devices grows, traditional security solutions may struggle to scale effectively to protect large IoT ecosystems.

The new horizons include the following:

  • AI and Machine Learning: Algorithms can be employed to detect abnormal behavior patterns in IoT networks, allowing for proactive threat detection and response.
  • Zero-Trust Architecture: This approach moves away from the traditional perimeter-based security model.
  • Edge Computing: It reduces latency and bandwidth requirements while potentially improving security by minimizing data exposure to the cloud.
  • Regulatory Frameworks: Compliance with government and industry standards can help ensure a baseline level of security across IoT deployments.

Contributions are welcomed in any of the above-mentioned or other related areas.

Dr. Denis Reilly
Guest Editor

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Keywords

  • device vulnerabilities
  • data privacy
  • interoperability
  • scalability
  • AI and machine learning
  • edge computing
  • regulatory frameworks

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Published Papers (1 paper)

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Research

26 pages, 3882 KiB  
Article
A Network Performance Analysis of MQTT Security Protocols with Constrained Hardware in the Dark Net for DMS
by Antonio Francesco Gentile, Davide Macrì, Domenico Luca Carnì, Emilio Greco and Francesco Lamonaca
Appl. Sci. 2024, 14(18), 8501; https://doi.org/10.3390/app14188501 - 20 Sep 2024
Cited by 1 | Viewed by 2269
Abstract
In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic [...] Read more.
In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments. Full article
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