applsci-logo

Journal Browser

Journal Browser

Intelligent Systems and Information Security

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

Deadline for manuscript submissions: 20 October 2025 | Viewed by 2034

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-661 Warsaw, Poland
Interests: network security; visible light communication; IoT security; risk assessment and management

E-Mail Website
Guest Editor
Institute for Language and Speech Processing, Athena Research Centre, Kimmeria University Campus, 67100 Xanthi, Greece
Interests: privacy-enhancing technologies (PETs); information security; distributed ledger technologies (DLTs); personal data management; cryptographic protocols; health informatics; information retrieval; social networks analysis; ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent Systems refer to software systems that exhibit behaviors perceived as intelligent and capable of performing tasks typically requiring human intelligence. These systems leverage artificial intelligence (AI) and Machine Learning (ML) techniques to autonomously make decisions, solve problems, learn from experiences, and adapt to new situations.

Intelligent Systems are increasingly applied in Information Security to enhance the detection, prevention, and response to various cyber threats. Intelligent Systems can analyze vast amounts of data in real-time to identify patterns and anomalies that might indicate security breaches, malware, and other threats. When a security threat is detected, intelligent systems can automatically take actions such as isolating affected systems, blocking malicious IP addresses, or initiating incident response protocols. Intelligent systems also track and analyze user behavior to detect insider threats or compromised accounts based on deviations from normal activity patterns.

This Special Issue, “Intelligent Systems and Information Security”, welcomes submissions of recent and original research work on this promising application area. The call is open to a broad thematic range of papers covering recent applications of AI and ML to intrusion detection and prevention, threat intelligence and incident response, OPSEC, secure software development, etc.

Recommended topics include, but are not limited to, the following:

  • Cloud and network security;
  • Endpoint security;
  • IoT security;
  • Critical Infrastructures security;
  • Mobile/wireless/5G security;
  • Biometric techniques;
  • Intrusion detection and prevention;
  • Operating system and application fingerprinting;
  • Risk assessment and risk management;
  • Vulnerability classification and management;
  • Threat intelligence;
  • Side-channel attacks;
  • Automated incident response;
  • User behavior analytics;
  • Fraud detection;
  • Digital forensics;
  • Security operations;
  • IT auditing and ethical hacking;
  • Software development security, including (web) applications, operating systems, and fuzz-testing;
  • Secure voting.

Dr. Grzegorz J. Blinowski
Dr. George Drosatos
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

  • information security
  • threat intelligence
  • cloud and network security
  • automated incident response
  • security operations
  • intelligent systems
  • artificial intelligence
  • machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 3138 KiB  
Article
Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
by Serhii Semenov, Magdalena Krupska-Klimczak, Roman Czapla, Beata Krzaczek, Svitlana Gavrylenko, Vadim Poltorazkiy and Zozulia Vladislav
Appl. Sci. 2025, 15(8), 4243; https://doi.org/10.3390/app15084243 - 11 Apr 2025
Viewed by 214
Abstract
This paper examines traditional machine learning algorithms, neural networks, and the benefits of utilizing ensemble models. Data preprocessing methods for improving the quality of classification models are considered. To balance the classes, Undersampling, Oversampling, and their combination (Over + Undersampling) algorithms are explored. [...] Read more.
This paper examines traditional machine learning algorithms, neural networks, and the benefits of utilizing ensemble models. Data preprocessing methods for improving the quality of classification models are considered. To balance the classes, Undersampling, Oversampling, and their combination (Over + Undersampling) algorithms are explored. A procedure for reducing feature correlation is proposed. Classification models based on meta-algorithms such as SVM, KNN Naive Bayes, Perceptron, Bagging, Random Forest, AdaBoost, and Gradient Boosting have been thoroughly investigated. The settings of the base classifiers and meta-algorithm parameters have been optimized. The best result was obtained by using an ensemble classifier based on the Random Forest algorithm. Thus, an intrusion detection method based on the preprocessing of highly correlated and imbalanced data has been proposed. The scientific novelty of the obtained results lies in the integrated use of the developed procedure for reducing feature correlation, the application of the SMOTEENN data balancing method, the selection of an appropriate classifier, and the fine tuning of its parameters. The integration of these procedures and methods resulted in a higher F1 score, reduced training time, and faster recognition speed for the model. This allows us to recommend this method for practical use to improve the quality of network intrusion detection. Full article
(This article belongs to the Special Issue Intelligent Systems and Information Security)
Show Figures

Figure 1

18 pages, 572 KiB  
Article
Infrastructure and Tools for Testing the Vulnerability of Control Systems to Cyberattacks: A Coal Mine Industrial Facility Case
by Sebastian Plamowski, Patryk Chaber, Maciej Ławryńczuk, Robert Nebeluk, Ewa Niewiadomska-Szynkiewicz, Jakub Suchorab, Krzysztof Zarzycki, Adam Kozakiewicz and Andrzej Stachurski
Appl. Sci. 2024, 14(23), 11325; https://doi.org/10.3390/app142311325 - 4 Dec 2024
Viewed by 1233
Abstract
Testing the vulnerability of information systems to cyberattacks is essential to ensure the operational security of organizations and industrial processes. In particular, it is essential to ensure the resilience of industrial processes, as a possible cyberattack can lead to process malfunctions and even [...] Read more.
Testing the vulnerability of information systems to cyberattacks is essential to ensure the operational security of organizations and industrial processes. In particular, it is essential to ensure the resilience of industrial processes, as a possible cyberattack can lead to process malfunctions and even process shutdowns, which can lead to substantial economic losses. The possibility of various attacks, e.g., ransomware, phishing, or advanced persistent threats (APTs), requires the evaluation of the effectiveness of cyberattack detection and incident response mechanisms. In industry, it is often impossible to carry out this type of test without risking system disruption, making it difficult to assess the true effectiveness of security features. This article discusses the issues concerned with testing the cyber resilience of a system operating in a real coal mine. First, this work briefly presents the hardware and software architecture used in the coal mine. Secondly, it describes the problem of replicating a real system in the laboratory and the necessary tools and methods used to implement a resilient system architecture. Finally, the scenarios of cyberattacks are detailed, and the obtained results are discussed. Full article
(This article belongs to the Special Issue Intelligent Systems and Information Security)
Show Figures

Figure 1

Back to TopTop