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Symmetry
Special Issues
Security Requirements and Challenges of Distributed Intelligent Devices and Applications
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Security Requirements and Challenges of Distributed Intelligent Devices and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3939

Special Issue Editors

Dr. Yu Jiang

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
Interests: IoT security; physical layer security
Prof. Dr. Jinbo Xiong

E-Mail Website
Guest Editor
College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China
Interests: IoT security; privacy protection; big data security; secure deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Shang Gao

E-Mail Website
Guest Editor
Department of Computing, The Hong Kong Polytechnic University, Hong Kong 000000, China
Interests: zero-knowledge proofs; zkSNARK; recursive SNARK
Dr. Yuexiu Xing

E-Mail Website
Guest Editor
The School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: physical layer security; radio frequency fingerprint; authentication; network security

Special Issue Information

Dear Colleagues,

Distributed and centralized resources exhibit symmetric and asymmetric characteristics. Distributed resource scenarios are wide-ranging, but there is a lack of security protection in the whole process of distributed resource aggregation and centralized regulation. Therefore, we encourage the submission of contributions in areas such as access authentication, data security or device vulnerabilities.

Dr. Yu Jiang
Prof. Dr. Jinbo Xiong
Dr. Shang Gao
Dr. Yuexiu Xing
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. Symmetry 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 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

  • distributed device security
  • physical security of intelligent devices
  • power terminal security
  • device vulnerabilities
  • device data security
  • secure machine learning technique for data analysis
  • access authentication for distributed device
  • multi-agent devices security
  • new security challenges in distributed devices

Published Papers (4 papers)

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Research

21 pages, 6458 KiB  
Article
Identification of IoT Devices Based on Hardware and Software Fingerprint Features
by Yu Jiang, Yufei Dou and Aiqun Hu
Symmetry 2024, 16(7), 846; https://doi.org/10.3390/sym16070846 - 4 Jul 2024
Viewed by 490
Abstract
Unauthenticated device access to a network presents substantial security risks. To address the challenges of access and identification for a vast number of devices with diverse functions in the era of the Internet of things (IoT), we propose an IoT device identification method [...] Read more.
Unauthenticated device access to a network presents substantial security risks. To address the challenges of access and identification for a vast number of devices with diverse functions in the era of the Internet of things (IoT), we propose an IoT device identification method based on hardware and software fingerprint features. This approach aims to achieve comprehensive “hardware–software–user” authentication. First, by extracting multimodal hardware fingerprint elements, we achieve identity authentication at the device hardware level. The time-domain and frequency-domain features of the device’s transient signals are extracted and further learned by a feature learning network to generate device-related time-domain and frequency-domain feature representations. These feature representations are fused using a splicing operation, and the fused features are input into the classifier to identify the device’s hardware attribute information. Next, based on the interaction traffic, behavioral information modeling and sequence information modeling are performed to extract the behavioral fingerprint elements of the device, achieving authentication at the software level. Experimental results demonstrate that the method proposed in this paper exhibits a high detection efficacy, achieving 99% accuracy in both software and hardware level identification. Full article
(This article belongs to the Special Issue Security Requirements and Challenges of Distributed Intelligent Devices and Applications)
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21 pages, 1048 KiB  
Article
Res-DFNN: An NN-Based Device Fingerprint Extraction Method Using Network Packet Data
by Yinan Zhong, Mingyu Pan, Yanjiao Chen and Wenyuan Xu
Symmetry 2024, 16(4), 443; https://doi.org/10.3390/sym16040443 - 6 Apr 2024
Viewed by 664
Abstract
The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. [...] Read more.
The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios. Full article
(This article belongs to the Special Issue Security Requirements and Challenges of Distributed Intelligent Devices and Applications)
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22 pages, 379 KiB  
Article
A 3C Authentication: A Cross-Domain, Certificateless, and Consortium-Blockchain-Based Authentication Method for Vehicle-to-Grid Networks in a Smart Grid
by Qianhao Miao, Tianyu Ren, Jiahan Dong, Yanjiao Chen and Wenyuan Xu
Symmetry 2024, 16(3), 336; https://doi.org/10.3390/sym16030336 - 11 Mar 2024
Viewed by 1108
Abstract
As an important component of the smart grid, vehicle-to-grid (V2G) networks can deliver diverse auxiliary services and enhance the overall resilience of electrical power systems. However, V2G networks face two main challenges due to a large number of devices that connect to it. [...] Read more.
As an important component of the smart grid, vehicle-to-grid (V2G) networks can deliver diverse auxiliary services and enhance the overall resilience of electrical power systems. However, V2G networks face two main challenges due to a large number of devices that connect to it. First, V2G networks suffer from serious security threats, such as doubtful authenticity and privacy leakage. Second, the efficiency will decrease significantly due to the massive requirements of authentication. To tackle these problems, this paper proposes a cross-domain authentication scheme for V2G networks based on consortium blockchain and certificateless signature technology. Featuring decentralized, open, and transparent transactions that cannot be tampered with, this scheme achieves good performance on both security and efficiency, which proves to be suitable for V2G scenarios in the smart grid. Full article
(This article belongs to the Special Issue Security Requirements and Challenges of Distributed Intelligent Devices and Applications)
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19 pages, 2093 KiB  
Article
Program Behavior Dynamic Trust Measurement and Evaluation Based on Data Analysis
by Shuai Wang, Aiqun Hu, Tao Li and Shaofan Lin
Symmetry 2024, 16(2), 249; https://doi.org/10.3390/sym16020249 - 17 Feb 2024
Viewed by 920
Abstract
Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security [...] Read more.
Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security protection methods for industrial control terminals have coarse detection granularity, and are unable to effectively detect and prevent attacks, lacking real-time responsiveness to attack events. Therefore, this paper proposes a real-time dynamic credibility evaluation mechanism based on program behavior, which integrates the matching and symmetry ideas of credibility evaluation. By conducting a real-time dynamic credibility evaluation of function call sequences and system call sequences during program execution, the credibility of industrial control terminal application program behavior can be judged. To solve the problem that the system calls generated during program execution are unstable and difficult to measure, this paper proposes a partition-based dynamic credibility evaluation method, dividing program behavior during runtime into function call behavior and system call behavior within function intervals. For function call behavior, a sliding window-based function call sequence benchmark library construction method is proposed, which matches and evaluates real-time measurement results based on the benchmark library, thereby achieving symmetry between the benchmark library and the measured data. For system call behavior, a maximum entropy system call model is constructed, which is used to evaluate the credibility of system call sequences. Experiment results demonstrate that our method performs better in both detection success rate and detection speed compared to the existing methods. Full article
(This article belongs to the Special Issue Security Requirements and Challenges of Distributed Intelligent Devices and Applications)
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