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Internet of Things (IoT) Privacy and Security in the Age...
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Internet of Things (IoT) Privacy and Security in the Age of Big Data

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1290

Special Issue Editors

Prof. Dr. Hongsong Chen

E-Mail Website
Guest Editor
School of Computer and Communication Engineering，University of Science and Technology Beijing, Beijing 100083, China
Interests: network and information; security cloud; computing and cloud security; software engineering; Internet of Things; security embedded software development
Dr. Keshav Sood

E-Mail Website
Guest Editor
School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: modern network security (Software-defined networking, Internet of Things, and 5G)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of the Internet of things (IoT), privacy and security have become the primary problems in IoT applications in the big data era. This Special Issue discusses the current challenges and solutions to secure IoT devices and applications to protect the security and privacy of all kinds of users’ data. A variety of IoT security and privacy solutions for protecting IoT data at the device layer, the IoT platform layer, and the IoT application layer have been developed. Ensuring end-to-end security and privacy across these IoT layers is a great challenge in IoT-related applications. The objective of this Special Issue is to explore the recent advances that address the fundamental and practical challenges related to security and privacy in IoT. High-quality original research and review articles in this area are expected. Potential topics include, but are not limited to, the following:

  • Privacy and Security in IoT and big data;
  • Secure data sharing in IoT;
  • Secure data analysis in IoT;
  • Privacy protection computing in IoT
  • Security, privacy and trust mechanisms in IoT and big data;
  • Privacy preserving in IoT;
  • Privacy enhanced computing in IoT;
  • Cyber attacks in IoT and big data;
  • Software security vulnerability analysis and mining in IoT;
  • Machine-learning and privacy protection in IoT and big data.

Prof. Dr. Hongsong Chen
Dr. Keshav Sood
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 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. 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 and privacy
  • IoT
  • big data
  • cyber attacks
  • information security and privacy-preserving

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Published Papers (2 papers)

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Research

21 pages, 452 KiB  
Article
LG-BiTCN: A Lightweight Malicious Traffic Detection Model Based on Federated Learning for Internet of Things
by Yuehua Huo, Junhan Chen, Yunhao Guo, Wei Liang and Jiyan Sun
Electronics 2025, 14(8), 1560; https://doi.org/10.3390/electronics14081560 - 11 Apr 2025
Viewed by 159
Abstract
The rapid growth of IoT devices has increased security attack behaviors, posing a challenge to IoT security. Some Federated-Learning-based detection methods have been widely used to detect malicious attacks in the IoT by analyzing network traffic; because of the nature of Federated Learning, [...] Read more.
The rapid growth of IoT devices has increased security attack behaviors, posing a challenge to IoT security. Some Federated-Learning-based detection methods have been widely used to detect malicious attacks in the IoT by analyzing network traffic; because of the nature of Federated Learning, these methods can protect user privacy and reduce bandwidth consumption. However, existing malicious traffic detection models are often complex, requiring significant computational resources for training. In addition, high-dimensional input features often contain redundant information, which further increases computational overhead. To mitigate this, many model lightweighting techniques are utilized, and many non-end-to-end dimensionality reduction methods are employed; however, these lightweighting methods still struggle to meet the computational demands, and these feature downscaling methods tend to compromise the model’s generalizability and accuracy. In addition, existing methods are unable to dynamically select long-term dependencies when extracting traffic time-series features, limiting the performance of the model when dealing with long time series. To address the above challenges, this paper proposes a lightweight malicious traffic detection model, named the lightweight gated bidirectional temporal convolutional network (LG-BiTCN), based on Federated Learning. First, we use global average pooling (GAP) and a pointwise convolutional layer as a classification module, significantly reducing the model’s parameter count. We also propose an end-to-end adaptive PCA dimension adjustment algorithm for automatic dimensionality reduction to reduce computational complexity and enhance model generalizability. Second, we incorporate gated convolution into the LG-BiTCN architecture, allowing for the dynamic selection of long-term dependencies, enhancing detection accuracy while maintaining computational efficiency. We evaluated the LG-BiTCN’s effectiveness by comparing it with three advanced baseline models on three generic datasets. The results show that the LG-BiTCN achieves over 99.6% accuracy while maintaining the lowest computational complexity. Additionally, in a Federated Learning setup, it requires just two communication rounds to reach 96.75% accuracy. Full article
(This article belongs to the Special Issue Internet of Things (IoT) Privacy and Security in the Age of Big Data)
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22 pages, 16421 KiB  
Article
A Lightweight Keystream Generator Based on Expanded Chaos with a Counter for Secure IoT
by Tung-Tsun Lee and Shyi-Tsong Wu
Electronics 2024, 13(24), 5019; https://doi.org/10.3390/electronics13245019 - 20 Dec 2024
Viewed by 683
Abstract
Stream ciphers are a type of symmetric encryption algorithm, and excel in speed and efficiency compared with block ciphers. They are applied in various applications, particularly in digital communications and real-time transmissions. In this paper, we propose lightweight chaotic keystream generators that utilize [...] Read more.
Stream ciphers are a type of symmetric encryption algorithm, and excel in speed and efficiency compared with block ciphers. They are applied in various applications, particularly in digital communications and real-time transmissions. In this paper, we propose lightweight chaotic keystream generators that utilize original one-dimensional (1D) chaotic maps with a counter to fit the requirement of a stream cipher for secure communications in the Internet of Things (IoT). The proposed chaotic scheme, referred to as expanded chaos, improves the limit of the chaotic range for the original 1D chaos. It can resist brute-force attacks, chosen-ciphertext attacks, guess-and-determine attacks, and other known attacks. We implement the proposed scheme on the IoT platform Raspberry Pi. Under NIST SP800-22 tests, the pass rates for the proposed improved chaotic maps with a counter and the proposed the mutual-coupled chaos are found to be at least about 90% and 92%, respectively. Full article
(This article belongs to the Special Issue Internet of Things (IoT) Privacy and Security in the Age of Big Data)
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