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Applied Sciences
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Privacy-Preserving and System Security Control Based on Machine Learning
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Privacy-Preserving and System Security Control Based on Machine Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 5557

Special Issue Editors

Dr. Emil Pricop

E-Mail Website
Guest Editor
Automatic Control, Computers & Electronics Department, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania
Interests: cybersecurity; industrial control system security; personal identification methods; Industry 4.0 technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Jaouhar Fattahi

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Laval University, Quebec, QC G1V 0A6, Canada
Interests: security; cryptographic protocols; anomaly/intrusion detection; reverse engineering; AI/ML/DL
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Grigore Stamatescu

E-Mail Website
Guest Editor
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: networked-embedded sensing; information processing; control engineering; building automation; smart city; data analytics; computational intelligence; industry and energy applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The widespread use of information and communication technologies has revolutionized our lives, offering unprecedented convenience and connectivity. However, this progress has also introduced significant challenges to privacy preservation and system security. Machine learning techniques have emerged as powerful tools capable of addressing the evolving security and privacy challenges.

This Special Issue on “Privacy-Preserving and System Security Control Based on Machine Learning” invites original research contributions and review articles to explore the latest advancements and applications of machine learning for privacy preservation and system security. We welcome submissions that cover a broad range of topics, including but not limited to the following:

  • Privacy-preserving machine learning algorithms;
  • Differential privacy;
  • Adversarial machine learning for security control;
  • Anomaly detection using machine learning for system security;
  • Zero-day attack detection using machine learning;
  • Secure federated learning;
  • Privacy-enhancing technologies in machine learning.

We expect this Special Issue to provide a timely and significant platform for researchers and practitioners to present their latest findings and foster collaborations in this swiftly advancing field. We are looking forward to your contributions.

Dr. Emil Pricop
Dr. Jaouhar Fattahi
Dr. Grigore Stamatescu
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. Applied Sciences 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

  • privacy preserving
  • system security
  • machine learning
  • differential privacy
  • adversarial machine learning
  • secure federated learning
  • machine learning based attack detection

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Published Papers (3 papers)

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Research

24 pages, 416 KB  
Article
Zero-Day Ransomware Attack Detection Using Static Portable Executable Header Features
by Algimantas Venčkauskas, Vacius Jusas and Dominykas Barisas
Appl. Sci. 2025, 15(19), 10576; https://doi.org/10.3390/app151910576 - 30 Sep 2025
Viewed by 224
Abstract
Ransomware is one of the types of malware attacks that most severely affects financial institutions, since they cannot afford to lose their data or experience long-term disruptions. It is crucial for financial institutions to protect themselves from ransomware attacks. To fight zero-day ransomware [...] Read more.
Ransomware is one of the types of malware attacks that most severely affects financial institutions, since they cannot afford to lose their data or experience long-term disruptions. It is crucial for financial institutions to protect themselves from ransomware attacks. To fight zero-day ransomware attacks that are previously unseen attacks, we have presented a method that uses the static header features of portable executables. The method forms a comprehensive static feature set that includes the header fields of portable executables, count of dynamic link libraries (DLLs), DLL average, DLL list, function call average, and a measure of section content randomness. In order to make a compact feature set, a threshold was applied to three feature sets: portable executable header, DLL features, and section randomness. To determine the DLL average usage, the Tanimoto coefficient was applied to measure DLL similarity. The same procedure was applied to determine the function call average. The Chi-square test was applied to measure the section content randomness of portable executables. A stacking classifier was applied to measure the performance of the developed feature set. A publicly available dataset was used for the experiments. The results for the detection of zero-day attacks demonstrated averages of 97.15% accuracy, 98.06% recall, and 92.74% F-measure. When compared with other methods using the same dataset, our proposed method provided slightly better performance for many ransomware families. Full article
(This article belongs to the Special Issue Privacy-Preserving and System Security Control Based on Machine Learning)
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17 pages, 1386 KB  
Article
Post-Hoc Categorization Based on Explainable AI and Reinforcement Learning for Improved Intrusion Detection
by Xavier Larriva-Novo, Luis Pérez Miguel, Victor A. Villagra, Manuel Álvarez-Campana, Carmen Sanchez-Zas and Óscar Jover
Appl. Sci. 2024, 14(24), 11511; https://doi.org/10.3390/app142411511 - 10 Dec 2024
Cited by 2 | Viewed by 1695
Abstract
The massive usage of Internet services nowadays has led to a drastic increase in cyberattacks, including sophisticated techniques, so that Intrusion Detection Systems (IDSs) need to use AP technologies to enhance their effectiveness. However, this has resulted in a lack of interpretability and [...] Read more.
The massive usage of Internet services nowadays has led to a drastic increase in cyberattacks, including sophisticated techniques, so that Intrusion Detection Systems (IDSs) need to use AP technologies to enhance their effectiveness. However, this has resulted in a lack of interpretability and explainability from different applications that use AI predictions, making it hard to understand by cybersecurity operators why decisions were made. To address this, the concept of Explainable AI (XAI) has been introduced to make the AI’s decisions more understandable at both global and local levels. This not only boosts confidence in the AI but also aids in identifying different attributes commonly used in cyberattacks for the exploitation of flaws or vulnerabilities. This study proposes two developments: first, the creation and evaluation of machine learning models for an IDS with the objective to use Reinforcement Learning (RL) to classify malicious network traffic, and second, the development of a methodology to extract multi-level explanations from the RL model to identify, detect, and understand how different attributes affect uncertain types of attack categories. Full article
(This article belongs to the Special Issue Privacy-Preserving and System Security Control Based on Machine Learning)
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14 pages, 503 KB  
Article
Robust Federated Learning for Mitigating Advanced Persistent Threats in Cyber-Physical Systems
by Ehsan Hallaji, Roozbeh Razavi-Far and Mehrdad Saif
Appl. Sci. 2024, 14(19), 8840; https://doi.org/10.3390/app14198840 - 1 Oct 2024
Cited by 1 | Viewed by 2162
Abstract
Malware triage is essential for the security of cyber-physical systems, particularly against Advanced Persistent Threats (APTs). Proper data for this task, however, are hard to come by, as organizations are often reluctant to share their network data due to security concerns. To tackle [...] Read more.
Malware triage is essential for the security of cyber-physical systems, particularly against Advanced Persistent Threats (APTs). Proper data for this task, however, are hard to come by, as organizations are often reluctant to share their network data due to security concerns. To tackle this issue, this paper presents a secure and distributed framework for the collaborative training of a global model for APT triage without compromising privacy. Using this framework, organizations can share knowledge of APTs without disclosing private data. Moreover, the proposed design employs robust aggregation protocols to safeguard the global model against potential adversaries. The proposed framework is evaluated using real-world data with 15 different APT mechanisms. To make the simulations more challenging, we assume that edge nodes have partial knowledge of APTs. The obtained results demonstrate that participants in the proposed framework can privately share their knowledge, resulting in a robust global model that accurately detects APTs with significant improvement across different model architectures. Under optimal conditions, the designed framework detects almost all APT scenarios with an accuracy of over 90 percent. Full article
(This article belongs to the Special Issue Privacy-Preserving and System Security Control Based on Machine Learning)
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