Recent Advances in Information Security and Data Privacy, 2nd Edition

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1374

Special Issue Editors


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Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: digital topology; network security; image processing; hole-counting technical reports
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
TSYS School of Computer Science, Columbus State University, 4225 University Avenue, Columbus, GA 31907, USA
Interests: database retrieval metrics; phylogeny search; biological networks

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: information security; data security and privacy; intrusion detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the fields of information security and data privacy have witnessed significant advancements, with novel techniques and methodologies emerging to safeguard computing infrastructure and critical information in an increasingly connected world. The scope of the Special Issue “Recent Advances in Information Security and Data Privacy, 2nd Edition”, covers the theory, applications, and implementations of information security and data privacy. It aims to showcase the latest research, developments, and advances in information security and data privacy, encompassing a wide range of topics from system and network security to security foundations and content protection, as well as from differential privacy and homomorphic encryption to federated learning and secure multi-party computation.

In this Special Issue, we are looking for original and creative research covering multiple fields of information security and data privacy. Research papers using theoretical, technical, and/or practical approaches, as well as survey papers, are all welcome. We invite submissions on topics that include, but are not limited to:

Topics:

  • Threat, intrusion, and anomaly detection for the Internet;
  • Operating system, database, and computing infrastructure security;
  • Internet, firewall, and mobile security;
  • Encryption and decryption algorithms in information security;
  • Privacy, access control, and authentication;
  • Anti-virus and anti-hacker techniques;
  • Detection and prevention of stepping-stone intrusion;
  • Differential privacy and its applications;
  • Homomorphic encryption and secure computation;
  • Federated learning and decentralized machine learning techniques;
  • Privacy-preserving machine learning algorithms and methodologies;
  • Blockchain and distributed ledger technologies for enhancing data privacy;
  • Post-quantum cryptography and its implications for data security;
  • Ethical considerations and fairness in privacy-preserving technologies;
  • Privacy in IoT and sensor networks;
  • User-centric privacy tools and technologies;
  • Compliance with data privacy regulations and legal frameworks.

Prof. Dr. Jianhua Yang
Dr. Hyrum Carroll
Dr. Linqiang Ge
Prof. Dr. Lixin Wang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • information security
  • network security
  • intrusion detection
  • data security
  • data privacy
  • quantum cryptography
  • mobile security
  • homomorphic encryption

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Published Papers (2 papers)

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Research

26 pages, 4848 KB  
Article
I Know What You Played Last Summer: Evaluating the Feasibility of Privacy Attacks in Massively Multiplayer Online Role-Playing Games
by Parisa Rahimi, George Spary, Amit Kumar Singh, Seyedali Pourmoafi, Xiaohang Wang and Alexios Mylonas
Electronics 2026, 15(9), 1888; https://doi.org/10.3390/electronics15091888 - 29 Apr 2026
Viewed by 381
Abstract
Massively Multiplayer Online Role-Playing Games (MMORPGs) increasingly rely on player-developed third-party tools to extend functionality and personalise gameplay, creating a complex software ecosystem that introduces both usability benefits and security risks. This study investigates whether such tools can be exploited as an attack [...] Read more.
Massively Multiplayer Online Role-Playing Games (MMORPGs) increasingly rely on player-developed third-party tools to extend functionality and personalise gameplay, creating a complex software ecosystem that introduces both usability benefits and security risks. This study investigates whether such tools can be exploited as an attack vector for cybercrime by designing and implementing a proof-of-concept add-on within a widely deployed commercial MMORPG using its native scripting and application programming interface. The developed tool supports automated player discovery, chat capture, target inspection, and local data persistence, enabling a systematic evaluation of how cyber-assisted and cyber-dependent crimes could be facilitated within the game client. Empirical testing demonstrates that while the platform’s protected execution model and interface restrictions prevent direct credential theft and remote code execution, the add-on architecture allows extensive behavioural data collection and social-engineering-relevant monitoring, making several forms of cyber-enabled crime technically feasible. These findings show that MMORPG add-on frameworks represent a non-trivial socio-technical attack vector in next-generation online platforms, where security depends not only on code isolation, but also on how user-generated extensions interact with human behaviour. The results highlight the need for architecture-aware security controls and governance mechanisms to mitigate emerging threats in large-scale, extensible virtual environments. Full article
(This article belongs to the Special Issue Recent Advances in Information Security and Data Privacy, 2nd Edition)
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24 pages, 4005 KB  
Article
Explainable Firewall Penetration Testing Method Employing Machine Learning
by Algimantas Venčkauskas, Jevgenijus Toldinas and Nerijus Morkevičius
Electronics 2026, 15(5), 1030; https://doi.org/10.3390/electronics15051030 - 1 Mar 2026
Viewed by 700
Abstract
Cyber adversaries are becoming more sophisticated, creating complex security challenges as digital services expand. The reliability of the firewall is of the utmost importance in the context of network security since it serves as the first line of protection. Penetration testing is an [...] Read more.
Cyber adversaries are becoming more sophisticated, creating complex security challenges as digital services expand. The reliability of the firewall is of the utmost importance in the context of network security since it serves as the first line of protection. Penetration testing is an approach used to evaluate the reliability of a firewall and improve security by uncovering exploitable flaws. Frequently, penetration testing solutions are developed using machine learning, and it is of the utmost importance to explain the obtained results during the penetration testing. The emergence of explainable AI (XAI) addresses transparency in ML models, which is essential for informed cybersecurity decisions. Additionally, effective penetration testing reports are crucial for organizations, helping them comprehend and address vulnerabilities with tailored mitigation strategies. This study contributes to firewall security by developing an explainable penetration testing method, which includes two machine learning classification models: a binary model for detecting attacks and a multiclass model for identifying attack types with an explainability feature. This research introduces a novel explainability method that emphasizes significant features related to attack types based on multiclass predictions and proposes an approach using the extended System Security Assurance Ontology (SSAO) to clarify vulnerabilities and suggest alternative mitigation strategies. After evaluating numerous ML algorithms for the CIC-IDS2017 dataset, the Fine Tree model was considered to have the greatest performance. For the binary model, it achieved a validation accuracy of 99.7%, while for the multiclass model, it achieved a validation accuracy of 99.6%. Both models were used to test the firewall for vulnerabilities. Firewall penetration testing using the binary model achieves an accuracy of 82.1%, while the multiclass model achieves an accuracy of 78.7%. Full article
(This article belongs to the Special Issue Recent Advances in Information Security and Data Privacy, 2nd Edition)
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