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Network Security in the Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1719

Special Issue Editor


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Guest Editor
School of Science, Edith Cowan University, Joondalup, Perth, WA 6027, Australia
Interests: cyber security; privacy; Internet of Things; machine learning; network forensics

Special Issue Information

Dear Colleagues, 

The Internet of Things (IoT) has evolved to be the dominant paradigm for connected devices today. It covers a myriad of devices from wearables (e.g., smart watches) to transport (e.g., UAVs and vehicular networks) to smart meters, used in hospitals, manufacturing, water, power, and many other systems. The IoT aims to add value not just to consumers but also to service providers, but the security of data transferred across IoT networks remains an issue. Heretofore, unenvisaged use cases, coupled with a wide variety of (often heterogeneous) devices, network protocols, and standards, have led to a broadening of the attack surface and a concomitant increase in vulnerabilities. The devices are at risk, data transferred are potentially subject to information leakage, and the flow-on effects test the resilience of IoT networks; therefore, effective network security is paramount. Mitigating threats is particularly challenging when the networks are part of critical infrastructure or are connected to safety-critical systems. 

This Special Issue aims to collect original research and review articles on advances, technologies, solutions, applications, and challenges in the field of network security, centred on the IoT. 

Potential topics include, but are not limited to, the following: 

  • Federated/collaborative learning in IoT network security;
  • Machine/deep learning applications for IoT network security;
  • Data analytics for IoT network security;
  • IoT Edge Computing and network security;
  • Security across heterogeneous networks;
  • Secure routing in IoT networks;
  • Vulnerability assessment of IoT devices or systems;
  • Explainable AI models for IoT network security;
  • Supply chain security;
  • Trust models for devices in IoT networks;
  • Quantum applications for IoT network security. 

Dr. Michael N. Johnstone
Guest Editor

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. Sensors 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 2600 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

  • Internet of Things
  • sensor networks
  • network security
  • intrusion detection
  • distributed learning

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Published Papers (1 paper)

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Research

35 pages, 641 KiB  
Article
A Scalable Approach to Internet of Things and Industrial Internet of Things Security: Evaluating Adaptive Self-Adjusting Memory K-Nearest Neighbor for Zero-Day Attack Detection
by Promise Ricardo Agbedanu, Shanchieh Jay Yang, Richard Musabe, Ignace Gatare and James Rwigema
Sensors 2025, 25(1), 216; https://doi.org/10.3390/s25010216 - 2 Jan 2025
Cited by 1 | Viewed by 1048
Abstract
The Internet of Things (IoT) and Industrial Internet of Things (IIoT) have drastically transformed industries by enhancing efficiency and flexibility but have also introduced substantial cybersecurity risks. The rise of zero-day attacks, which exploit unknown vulnerabilities, poses significant threats to these interconnected systems. [...] Read more.
The Internet of Things (IoT) and Industrial Internet of Things (IIoT) have drastically transformed industries by enhancing efficiency and flexibility but have also introduced substantial cybersecurity risks. The rise of zero-day attacks, which exploit unknown vulnerabilities, poses significant threats to these interconnected systems. Traditional signature-based intrusion detection systems (IDSs) are insufficient for detecting such attacks due to their reliance on pre-defined attack signatures. This study investigates the effectiveness of Adaptive SAMKNN, an adaptive k-nearest neighbor with self-adjusting memory (SAM), in detecting and responding to various attack types in Internet of Things (IoT) environments. Through extensive testing, our proposed method demonstrates superior memory efficiency, with a memory footprint as low as 0.05 MB, while maintaining high accuracy and F1 scores across all datasets. The proposed method also recorded a detection rate of 1.00 across all simulated zero-day attacks. In scalability tests, the proposed technique sustains its performance even as data volume scales up to 500,000 samples, maintaining low CPU and memory consumption. However, while it excels under gradual, recurring, and incremental drift, its sensitivity to sudden drift highlights an area for further improvement. This study confirms the feasibility of Adaptive SAMKNN as a real-time, scalable, and memory-efficient solution for IoT and IIoT security, providing reliable anomaly detection without overwhelming computational resources. Our proposed method has the potential to significantly increase the security of IoT and IIoT environments by enabling the real-time, scalable, and efficient detection of sophisticated cyber threats, thereby safeguarding critical interconnected systems against emerging vulnerabilities. Full article
(This article belongs to the Special Issue Network Security in the Internet of Things)
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