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Electronics
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New Insights in Information Security and Data Privacy: Challenges and...
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New Insights in Information Security and Data Privacy: Challenges and Solutions

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 8977

Special Issue Editors

Dr. Shancang Li

E-Mail Website
Guest Editor
Faulty of Engineering, Department of Civil Engineering, University of Bristol, University Walk, Clifton BS8, UK
Interests: Cyber Security; Data Privacy; Internet of Things; Artificial Intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Shanshan Zhao

E-Mail Website
Guest Editor
Engineering Modelling and Simulation (EMS) Research Group, University of the West of England, Bristol, UK
Interests: industry critical system; data protection; machine learning; AI

Special Issue Information

Dear Colleagues,

A surge in technological advancements has granted users unprecedented access to a wealth of data. While this brings numerous benefits, it also catapults cybersecurity and data privacy into the spotlight. Cutting-edge technologies such as AI, IoT, industry-critical systems, and generative AI, though offering transformative advantages, are now prime targets for cyber attackers. The landscape is evolving as cybercriminals adopt increasingly sophisticated tactics, harnessing advanced technologies to exploit vulnerabilities within information systems. This underscores the urgent need for robust cybersecurity measures to safeguard sensitive data and ensure the integrity of our interconnected digital ecosystem. The purpose of this Special Issue is to bring together the most recent research results on information security and data privacy, discuss and understand the challenges and opportunities, and find possible solutions.  The topics of interest of this Special Issue include (but are not limited to) the following:

Topics:

(1) Machine learning and AI in security;

(2) Data privacy and data quality assurance;

(3) Privacy enhancing technology;

(4) Security in smart technologies;

(5) IoT security;

(6) Cybersecurity awareness and training;

(7) Healthcare privacy;

(8) Vulnerability analysis in information system;

(9) Regulatory compliance;

(10) Advanced secure information processing.

Dr. Shancang Li
Dr. Shanshan Zhao
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

  • cyber security
  • IoT
  • AI
  • privacy preserving
  • data quality
  • incident response
  • threat intelligence

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Published Papers (6 papers)

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Research

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24 pages, 2024 KiB  
Article
An IoT Featureless Vulnerability Detection and Mitigation Platform
by Sarah Bin Hulayyil and Shancang Li
Electronics 2025, 14(7), 1459; https://doi.org/10.3390/electronics14071459 - 4 Apr 2025
Viewed by 415
Abstract
With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) [...] Read more.
With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) algorithms, which are deep learning and large language models, to uncover IoT security vulnerabilities based on network traffic data directly without manual feature selection. The platform correctly identifies vulnerable and secure IoT devices just by raw network traffic! Experimental results show that the proposed study detects vulnerability with great accuracy by using pre-trained deep learning and LLM models, which facilitates direct extraction of vulnerability features from the dataset and therefore helps speed up the identification process. In addition, the design of the platform ensures that the models are accessible and can be easily applied by users with a user-friendly interface. Furthermore, the models with small sizes, 277.5 MB and 334 MB for the deep learning model and the LLM model, respectively, illustrated the potential use of the detection tool in practical settings. The ability to defend large-scale, diversified IoT ecosystems efficiently and in a scalable way by installing thousands of software manifestations quickly while exposing new applications to growing cyber threats is made possible by this significant advancement in the field of IoT security. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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16 pages, 1627 KiB  
Article
Self-MCKD: Enhancing the Effectiveness and Efficiency of Knowledge Transfer in Malware Classification
by Hyeon-Jin Jeong, Han-Jin Lee, Gwang-Nam Kim and Seok-Hwan Choi
Electronics 2025, 14(6), 1077; https://doi.org/10.3390/electronics14061077 - 8 Mar 2025
Viewed by 380
Abstract
As malware continues to evolve, AI-based malware classification methods have shown significant promise in improving the malware classification performance. However, these methods lead to a substantial increase in computational complexity and the number of parameters, increasing the computational cost during the training process. [...] Read more.
As malware continues to evolve, AI-based malware classification methods have shown significant promise in improving the malware classification performance. However, these methods lead to a substantial increase in computational complexity and the number of parameters, increasing the computational cost during the training process. Moreover, the maintenance cost of these methods also increases, as frequent retraining and transfer learning are required to keep pace with evolving malware variants. In this paper, we propose an efficient knowledge distillation technique for AI-based malware classification methods called Self-MCKD. Self-MCKD transfers output logits that are separated into the target class and non-target classes. With the separation of the output logits, Self-MCKD enables efficient knowledge transfer by assigning weighted importance to the target class and non-target classes. Also, Self-MCKD utilizes small and shallow AI-based malware classification methods as both the teacher and student models to overcome the need to use large and deep methods as the teacher model. From the experimental results using various malware datasets, we show that Self-MCKD outperforms the traditional knowledge distillation techniques in terms of the effectiveness and efficiency of its malware classification. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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21 pages, 4458 KiB  
Article
Video-Wise Just-Noticeable Distortion Prediction Model for Video Compression with a Spatial–Temporal Network
by Huanhua Liu, Shengzong Liu, Jianyu Xiao, Dandan Xu and Xiaoping Fan
Electronics 2024, 13(24), 4977; https://doi.org/10.3390/electronics13244977 - 18 Dec 2024
Viewed by 900
Abstract
Just-Noticeable Difference (JND) in an image/video refers to the maximum difference that the human visual system cannot perceive, which has been widely applied in perception-guided image/video compression. In this work, we propose a Binary Decision-based Video-Wise Just-Noticeable Difference Prediction Method (BD-VW-JND-PM) with deep [...] Read more.
Just-Noticeable Difference (JND) in an image/video refers to the maximum difference that the human visual system cannot perceive, which has been widely applied in perception-guided image/video compression. In this work, we propose a Binary Decision-based Video-Wise Just-Noticeable Difference Prediction Method (BD-VW-JND-PM) with deep learning. Firstly, we model the VW-JND prediction problem as a binary decision process to reduce the inferring complexity. Then, we propose a Perceptually Lossy/Lossless Predictor for Compressed Video (PLLP-CV) to identify whether the distortion can be perceived or not. In the PLLP-CV, a Spatial–Temporal Network-based Perceptually Lossy/Lossless predictor (ST-Network-PLLP) is proposed for key frames by learning the spatial and temporal distortion features, and a threshold-based integration strategy is proposed to obtain the final results. Experimental results evaluated on the VideoSet database show that the mean prediction accuracy of PLLP-CV is about 95.6%, and the mean JND prediction error is 1.46 in QP and 0.74 in Peak-to-Noise Ratio (PSNR), which achieve 15% and 14.9% improvements, respectively. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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15 pages, 438 KiB  
Article
Using Generative AI Models to Support Cybersecurity Analysts
by Štefan Balogh, Marek Mlynček, Oliver Vraňák and Pavol Zajac
Electronics 2024, 13(23), 4718; https://doi.org/10.3390/electronics13234718 - 28 Nov 2024
Viewed by 1954
Abstract
One of the tasks of security analysts is to detect security vulnerabilities and ongoing attacks. There is already a large number of software tools that can help to collect security-relevant data, such as event logs, security settings, application manifests, and even the (decompiled) [...] Read more.
One of the tasks of security analysts is to detect security vulnerabilities and ongoing attacks. There is already a large number of software tools that can help to collect security-relevant data, such as event logs, security settings, application manifests, and even the (decompiled) source code of potentially malicious applications. The analyst must study these data, evaluate them, and properly identify and classify suspicious activities and applications. Fast advances in the area of Artificial Intelligence have produced large language models that can perform a variety of tasks, including generating text summaries and reports. In this article, we study the potential black-box use of LLM chatbots as a support tool for security analysts. We provide two case studies: the first is concerned with the identification of vulnerabilities in Android applications, and the second one is concerned with the analysis of security logs. We show how LLM chatbots can help security analysts in their work, but point out specific limitations and security concerns related to this approach. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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19 pages, 2027 KiB  
Article
T-Smade: A Two-Stage Smart Detector for Evasive Spectre Attacks Under Various Workloads
by Jiajia Jiao, Ran Wen and Yulian Li
Electronics 2024, 13(20), 4090; https://doi.org/10.3390/electronics13204090 - 17 Oct 2024
Cited by 2 | Viewed by 1060
Abstract
Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only [...] Read more.
Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only 59.8% for realistic applications, while it is much lower 27.52% for memory stress test. Therefore, this paper proposes a two-stage smart detector T-Smade designed for evasive Spectre attacks (e.g., evasive Spectre nop and evasive Spectre memory) under various workloads. T-Smade uses the first-stage detector to identify the type of workloads and then selects the appropriate second-stage detector, which uses four hardware performance counter events to characterize the high cache miss rate and low branch miss rate of Spectre attacks. More importantly, the second stage detector adds one dimension of reusing cache miss rate and branch miss rate to exploit the characteristics of various workloads to detect evasive Spectre attacks effectively. Furthermore, to achieve the good generalization for more unseen evasive Spectre attacks, the proposed classification detector T-Smade is trained by the raw data of Spectre attacks and non-attacks in different workloads using simple Multi-Layer Perception models. The comprehensive results demonstrate that T-Smade makes the average attack detection successful rate of evasive Spectre nop under different workload return from 27.52% to 95.42%, and that of evasive Spectre memory from 59.8% up to 100%. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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24 pages, 313 KiB  
Systematic Review
A Comprehensive Literature Review on Volatile Memory Forensics
by Ishrag Hamid and M. M. Hafizur Rahman
Electronics 2024, 13(15), 3026; https://doi.org/10.3390/electronics13153026 - 31 Jul 2024
Viewed by 3728
Abstract
Through a systematic literature review, which is considered the most comprehensive way to analyze the field of memory forensics, this paper investigates its development through past and current methodologies, as well as future trends. This paper systematically starts with an introduction to the [...] Read more.
Through a systematic literature review, which is considered the most comprehensive way to analyze the field of memory forensics, this paper investigates its development through past and current methodologies, as well as future trends. This paper systematically starts with an introduction to the key issues and a notable agenda of the research questions. Appropriate inclusion and exclusion criteria were then developed, and a deliberate search strategy was adopted to identify primary research studies aligned with the research question. The paper goes into specific details of six different memory categories, notably volatile memory, interpreting their advantages and the tactics used to retrieve the data. A detailed comparison with existing reviews and other relevant papers is made, forming a broader and wider picture of the research. The discussion summarizes the main findings, particularly the rise of more complex and advanced cyber threats and the necessity of more effective forensic methods for their investigation. This review pinpoints the possibilities for future study with the purpose of staying ahead in the evolving technological landscape. This overview is undoubtedly an essential resource for professionals and researchers working in digital forensics. It allows them to stay competent and provides enough insight into the current trends while marking the future direction in digital forensics methodology. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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