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Electronics
Special Issues
Theories and Technologies of Network, Data and Information Security
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Theories and Technologies of Network, Data and Information Security

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

Deadline for manuscript submissions: 15 May 2024 | Viewed by 6753

Special Issue Editor

Dr. Jong Wook Kim

E-Mail Website
Guest Editor
Department of Computer Science, Sangmyung University, 20 Hongjimoon-2gil, Seoul 030031, Korea
Interests: data security and privacy; privacy-enhancing technology; network security

Special Issue Information

Dear Colleagues,

Cyberattacks are on the rise in terms of both quantity and complexity, with attackers employing increasingly sophisticated methods to breach security systems and compromise sensitive information. The growing complexity of modern cyberattacks has led to increased focus from both researchers and practitioners in the field of cybersecurity. As a result, innovative technologies and methods are recently emerging to assist organizations and individuals in enhancing their ability to safeguard against the ever-increasing cyber threats.

Therefore, this Special Issue aims to explore new approaches and perspectives on network, data, and information security which are three critical and interrelated areas of cybersecurity that are essential for protecting organizations and individuals against a variety of possible cyber threats. Survey papers addressing relevant topics are also welcome. This Special Issue will focus on (but is not limited to) the following topics:

  • Data security;
  • Data privacy;
  • AI and machine learning for cybersecurity;
  • Secure communications;
  • Access control and identity management;
  • Cryptography;
  • Digital forensics;
  • AI-driven Security Systems;
  • IoT security and privacy;
  • Cybersecurity;
  • Blockchain;
  • Cyberthreat detection;
  • Big data security and reliability.

Dr. Jong Wook Kim
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. 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

  • data security
  • data privacy
  • AI and machine learning for cybersecurity
  • secure communications
  • access control and identity management 
  • cryptography
  • digital forensics
  • AI-driven security systems
  • IoT security and privacy
  • cybersecurity
  • blockchain
  • cyberthreat detection
  • big data security and reliability

Published Papers (6 papers)

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Research

23 pages, 662 KiB  
Article
A Memorable Communication Method Based on Cryptographic Accumulator
by Wenbao Jiang, Yongpan Wang and Shuai Ye
Electronics 2024, 13(6), 1081; https://doi.org/10.3390/electronics13061081 - 14 Mar 2024
Viewed by 389
Abstract
The traditional Internet has many security problems. It is difficult to guarantee the authenticity, integrity, and synchronization of message transmission, and it lacks a message-traceability mechanism, which is caused by its performance-oriented design. To address these problems, this paper proposes a memorable communication [...] Read more.
The traditional Internet has many security problems. It is difficult to guarantee the authenticity, integrity, and synchronization of message transmission, and it lacks a message-traceability mechanism, which is caused by its performance-oriented design. To address these problems, this paper proposes a memorable communication method based on cryptographic accumulators. In this method, both parties in the communication can verify the message data sent and received arbitrarily by virtue of the memory value. As long as a simple memory value comparison is performed, the strong consistency of all message data can be ensured. This method has the security advantages of synchronization, verification, traceability, and non-tamperability, as well as the performance advantages brought by batch signature and verification. In this paper, the memorable communication model, the memory function, and the memorable communication process are designed, and theoretical analysis shows that the memorable communication method has synchronization and traceability and can realize batch signature and authentication. In addition, a chain-key can be constructed based on a memory value to achieve key per-packet updating. Comparative analysis shows the transmission efficiency, traceability efficiency, and security performance of the memorable communication method. Full article
(This article belongs to the Special Issue Theories and Technologies of Network, Data and Information Security)
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21 pages, 6926 KiB  
Article
An Accurate and Invertible Sketch for Super Spread Detection
by Zheng Zhang, Jie Lu, Quan Ren, Ziyong Li, Yuxiang Hu and Hongchang Chen
Electronics 2024, 13(1), 222; https://doi.org/10.3390/electronics13010222 - 03 Jan 2024
Viewed by 791
Abstract
Super spread detection has been widely applied in network management, recommender systems, and cyberspace security. It is more complicated than heavy hitter owing to the requirement of duplicate removal. Accurately detecting a super spread in real-time with small memory demands remains a nontrivial [...] Read more.
Super spread detection has been widely applied in network management, recommender systems, and cyberspace security. It is more complicated than heavy hitter owing to the requirement of duplicate removal. Accurately detecting a super spread in real-time with small memory demands remains a nontrivial yet challenging issue. The previous work either had low accuracy or incurred heavy memory overhead and could not provide a precise cardinality estimation. This paper designed an invertible sketch for super spread detection with small memory demands and high accuracy. It introduces a power-weakening increment strategy that creates an environment encouraging sufficient competition at the early stages of discriminating a super spread and amplifying the comparative dominance to maintain accuracy. Extensive experiments have been performed based on actual Internet traffic traces and recommender system datasets. The trace-driven evaluation demonstrates that our sketch actualizes higher accuracy in super spread detection than state-of-the-art sketches. The super spread cardinality estimation error is 2.6–19.6 times lower than that of the previous algorithms. Full article
(This article belongs to the Special Issue Theories and Technologies of Network, Data and Information Security)
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14 pages, 2260 KiB  
Article
A BiLSTM–Transformer and 2D CNN Architecture for Emotion Recognition from Speech
by Sera Kim and Seok-Pil Lee
Electronics 2023, 12(19), 4034; https://doi.org/10.3390/electronics12194034 - 25 Sep 2023
Cited by 3 | Viewed by 1723
Abstract
The significance of emotion recognition technology is continuing to grow, and research in this field enables artificial intelligence to accurately understand and react to human emotions. This study aims to enhance the efficacy of emotion recognition from speech by using dimensionality reduction algorithms [...] Read more.
The significance of emotion recognition technology is continuing to grow, and research in this field enables artificial intelligence to accurately understand and react to human emotions. This study aims to enhance the efficacy of emotion recognition from speech by using dimensionality reduction algorithms for visualization, effectively outlining emotion-specific audio features. As a model for emotion recognition, we propose a new model architecture that combines the bidirectional long short-term memory (BiLSTM)–Transformer and a 2D convolutional neural network (CNN). The BiLSTM–Transformer processes audio features to capture the sequence of speech patterns, while the 2D CNN handles Mel-Spectrograms to capture the spatial details of audio. To validate the proficiency of the model, the 10-fold cross-validation method is used. The methodology proposed in this study was applied to Emo-DB and RAVDESS, two major emotion recognition from speech databases, and achieved high unweighted accuracy rates of 95.65% and 80.19%, respectively. These results indicate that the use of the proposed transformer-based deep learning model with appropriate feature selection can enhance performance in emotion recognition from speech. Full article
(This article belongs to the Special Issue Theories and Technologies of Network, Data and Information Security)
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11 pages, 1455 KiB  
Article
Enhanced Speech Emotion Recognition Using DCGAN-Based Data Augmentation
by Ji-Young Baek and Seok-Pil Lee
Electronics 2023, 12(18), 3966; https://doi.org/10.3390/electronics12183966 - 20 Sep 2023
Cited by 4 | Viewed by 1503
Abstract
Although emotional speech recognition has received increasing emphasis in research and applications, it remains challenging due to the diversity and complexity of emotions and limited datasets. To address these limitations, we propose a novel approach utilizing DCGAN to augment data from the RAVDESS [...] Read more.
Although emotional speech recognition has received increasing emphasis in research and applications, it remains challenging due to the diversity and complexity of emotions and limited datasets. To address these limitations, we propose a novel approach utilizing DCGAN to augment data from the RAVDESS and EmoDB databases. Then, we assess the efficacy of emotion recognition using mel-spectrogram data by utilizing a model that combines CNN and BiLSTM. The preliminary experimental results reveal that the suggested technique contributes to enhancing the emotional speech identification performance. The results of this study provide directions for further development in the field of emotional speech recognition and the potential for practical applications. Full article
(This article belongs to the Special Issue Theories and Technologies of Network, Data and Information Security)
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16 pages, 5389 KiB  
Article
Fake Biometric Detection Based on Photoplethysmography Extracted from Short Hand Videos
by Byeongseon An, Hyeji Lim and Eui Chul Lee
Electronics 2023, 12(17), 3605; https://doi.org/10.3390/electronics12173605 - 26 Aug 2023
Cited by 1 | Viewed by 1038
Abstract
An array of authentication methods has emerged, underscoring the importance of addressing spoofing challenges arising from forgery and alteration. Previous studies utilizing palm biometrics have attempted to circumvent spoofing through geometric methods or the analysis of vein images. However, these approaches are inadequate [...] Read more.
An array of authentication methods has emerged, underscoring the importance of addressing spoofing challenges arising from forgery and alteration. Previous studies utilizing palm biometrics have attempted to circumvent spoofing through geometric methods or the analysis of vein images. However, these approaches are inadequate when faced with hand-printed photographs or in the absence of near-infrared sensors. In this study, we propose using remote photoplethysmography (rPPG) signals to tackle spoofing concerns in palm images captured in RGB environments. rPPG signals were extracted using video durations of 3, 5, and 7 s, and 30 features within the heart rate band were identified through frequency conversion. A support vector machine (SVM) model was trained with the processed features, yielding accuracies of 97.16%, 98.4%, and 97.28% for video durations of 3, 5, and 7 s, respectively. These features underwent dimensionality reduction through a principal component analysis (PCA), and the results were compared with the initial 30 features. Additionally, we evaluated the confusion matrix with zero false-positives for each video duration, finding that the overall accuracy experienced a decline of 1 to 3%. The 5 s video retained the highest accuracy with the smallest decrement, registering a value of 97.2%. Full article
(This article belongs to the Special Issue Theories and Technologies of Network, Data and Information Security)
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16 pages, 2328 KiB  
Article
Adapting Geo-Indistinguishability for Privacy-Preserving Collection of Medical Microdata
by Seungmin Song and Jongwook Kim
Electronics 2023, 12(13), 2793; https://doi.org/10.3390/electronics12132793 - 24 Jun 2023
Viewed by 680
Abstract
In the era of the Fourth Industrial Revolution, the increasing demand for data collection and sharing for analysis purposes has raised concerns regarding privacy violations. Protecting individual privacy during the collection and dissemination of sensitive information has emerged as a critical concern. In [...] Read more.
In the era of the Fourth Industrial Revolution, the increasing demand for data collection and sharing for analysis purposes has raised concerns regarding privacy violations. Protecting individual privacy during the collection and dissemination of sensitive information has emerged as a critical concern. In this paper, we propose a privacy-preserving framework for collecting users’ medical microdata, utilizing geo-indistinguishability (Geo-I), a concept based on well-known differential privacy. We adapt Geo-I, originally designed for protecting location information privacy, to collect medical microdata while minimizing the reduction in data utility. To mitigate the reduction in data utility caused by the perturbation mechanism of Geo-I, we propose a novel data perturbation technique that utilizes the prior distribution information of the data being collected. The proposed framework enables the collection of perturbed microdata with a distribution similar to that of the original dataset, even in scenarios that demand high levels of privacy protection, typically requiring significant perturbations to the original data. We evaluate the performance of our proposed algorithms using real-world data and demonstrate that our approach significantly outperforms existing methods, ensuring user privacy while preserving data utility in medical data collection. Full article
(This article belongs to the Special Issue Theories and Technologies of Network, Data and Information Security)
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