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Applied Sciences
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Artificial Intelligence and Cybersecurity: Challenges and Opportunities
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Artificial Intelligence and Cybersecurity: Challenges and Opportunities

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 February 2026 | Viewed by 5175

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. Khalil El-Khatib

E-Mail Website
Guest Editor
1. Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
2. Institute for Cybersecurity and Resilient Systems (ICRS), Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
Interests: cybersecurity; resilient systems; security and privacy issues in WSN; smart grid security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly transforming the cybersecurity landscape, presenting unprecedented opportunities and significant challenges. This Special Issue will explore this evolving relationship, delving into the latest research, innovations, and critical analyses at the intersection of AI and cybersecurity.

We welcome submissions that cover a broad range of topics, including, but not limited to, the following:

  • AI-based threat detection and response;
  • AI for security analytics and threat intelligence;
  • AI in malware detection and analysis;
  • Automated vulnerability assessment and penetration testing using AI;
  • AI for risk assessment and compliance management in cybersecurity;
  • Adversarial machine learning in cybersecurity;
  • Robustness and reliability of AI-based cybersecurity systems;
  • Privacy and ethical considerations in AI-driven cybersecurity.

We expect this Special Issue to provide a timely and significant platform for researchers and practitioners to present their latest findings and foster collaboration in this rapidly evolving field. We look forward to receiving your contributions.

Dr. Emil Pricop
Dr. Khalil El-Khatib
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

  • artificial intelligence
  • cybersecurity
  • AI-based cybersecurity
  • AI-based threat detection
  • threat intelligence
  • AI-based malware detection
  • adversarial machine learning
  • adversarial artificial intelligence
  • robust and reliable AI-based cybersecurity

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Published Papers (4 papers)

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Research

43 pages, 2944 KB  
Article
A Novel Approach to SPAM Detection in Social Networks-Light-ANFIS: Integrating Gradient-Based One-Sided Sampling and Random Forest-Based Feature Clustering Techniques with Adaptive Neuro-Fuzzy Inference Systems
by Oğuzhan Çıtlak, İsmail Atacak and İbrahim Alper Doğru
Appl. Sci. 2025, 15(18), 10049; https://doi.org/10.3390/app151810049 - 14 Sep 2025
Viewed by 112
Abstract
With today’s technological advancements and the widespread use of the Internet, social networking platforms that allow users to interact with each other are increasing rapidly. The popular social network X (formerly Twitter) has become a target for malicious actors, and spam is one [...] Read more.
With today’s technological advancements and the widespread use of the Internet, social networking platforms that allow users to interact with each other are increasing rapidly. The popular social network X (formerly Twitter) has become a target for malicious actors, and spam is one of its biggest challenges. The filters employed by such platforms to protect users struggle to keep up with evolving spam techniques, the diverse behaviors of platform users, the dynamic tactics of spam accounts, and the need for updates in spam detection algorithms. The literature shows that many effective solutions rely on computationally expensive methods that are limited by dataset constraints. This study addresses the spam challenges of social networks by proposing a novel detection framework, Light-ANFIS, which combines ANFIS with gradient-based one-side sampling (GOSS) and random forest-based clustering (RFBFC) techniques. The proposed approach employs the RFBFC technique to achieve efficient feature reduction, yielding an ANFIS model with reduced input requirements. This optimized ANFIS structure enables a simpler system configuration by minimizing parameter usage. In this context, dimensionality reduction enables a faster ANFIS training. The GOSS technique further accelerates ANFIS training by reducing the sample size without sacrificing accuracy. The proposed Light-ANFIS architecture was evaluated using three datasets: two public benchmarks and one custom dataset. To demonstrate the impact of GOSS, its performance was benchmarked against that of RFBFC-ANFIS, which relies solely on RFBFC. Experiments comparing the training durations of the Light-ANFIS and RFBFC-ANFIS architectures revealed that the GOSS technique improved the training time efficiency by 38.77% (Dataset 1), 40.86% (Dataset 2), and 38.79% (Dataset 3). The Light-ANFIS architecture has also achieved successful results in terms of accuracy, precision, recall, F1-score, and AUC performance metrics. The proposed architecture has obtained scores of 0.98748, 0.98821, 0.99091, 0.98956, and 0.98664 in Dataset 1; 0.98225, 0.97412, 0.99043, 0.98221, and 0.98233 in Dataset 2; and 0.98552, 0.98915, 0.98720, 0.98818, and 0.98503 in Dataset 3 for these performance metrics, respectively. The Light-ANFIS architecture has been observed to demonstrate performance above existing methods when compared with methods in studies using similar datasets and methodologies based on the literature. Even in Dataset 1 and Dataset 3, it achieved a slightly better performance in terms of confusion matrix metrics compared to current deep learning (DL)-based hybrid and fusion methods, which are known as high-performance complex models in this field. Additionally, the proposed model not only exhibits high performance but also features a simpler configuration than structurally equivalent models, providing it with a competitive edge. This makes it a valuable for safeguarding social media users from harmful content. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cybersecurity: Challenges and Opportunities)
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25 pages, 394 KB  
Article
SMART DShot: Secure Machine-Learning-Based Adaptive Real-Time Timing Correction
by Hyunmin Kim, Zahid Basha Shaik Kadu and Kyusuk Han
Appl. Sci. 2025, 15(15), 8619; https://doi.org/10.3390/app15158619 - 4 Aug 2025
Viewed by 501
Abstract
The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems [...] Read more.
The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cybersecurity: Challenges and Opportunities)
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22 pages, 580 KB  
Article
The Choice of Training Data and the Generalizability of Machine Learning Models for Network Intrusion Detection Systems
by Marcin Iwanowski, Dominik Olszewski, Waldemar Graniszewski, Jacek Krupski and Franciszek Pelc
Appl. Sci. 2025, 15(15), 8466; https://doi.org/10.3390/app15158466 - 30 Jul 2025
Viewed by 729
Abstract
Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained [...] Read more.
Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained on such datasets. This issue is crucial given the application of such a model to actual internet traffic because high-performance measures obtained on datasets do not necessarily imply similar efficiency on the real traffic. We propose a procedure consisting of cross-validation using various sets sharing some standard traffic classes combined with the t-SNE visualization. We apply it to investigate four well-known and widely used datasets: UNSW-NB15, CIC-CSE-IDS2018, BoT-IoT, and ToN-IoT. Our investigation reveals that the high accuracy of a model obtained on one set used for training is reproducible on others only to a limited extent. Moreover, benign traffic classes’ generalizability differs from harmful traffic. Given its application in the actual network environment, it implies that one needs to select the data used to train the ML model carefully to determine to what extent the classes present in the dataset used for training are similar to those in the real target traffic environment. On the other hand, merging datasets may result in more exhaustive data collection, consisting of a more diverse spectrum of training samples. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cybersecurity: Challenges and Opportunities)
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27 pages, 16245 KB  
Article
Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network
by Nisa Vuran Sarı, Mehmet Acı and Çiğdem İnan Acı
Appl. Sci. 2025, 15(9), 4775; https://doi.org/10.3390/app15094775 - 25 Apr 2025
Viewed by 1360
Abstract
As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, [...] Read more.
As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. This work presents a novel framework integrating API call sequences and DLL information into a unified, graph-based representation to analyze malware behavior comprehensively. The proposed model generates initial embeddings using Node2Vec, which uses a random walk approach to understand structural relationships between nodes. Graph Attention Network (GAT) then enhances these initial embeddings, which utilizes attention mechanisms to incorporate contextual dependencies and enhance semantic representations. Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. The dual role of GAT as a classifier and feature extractor is also analyzed to evaluate its impact on embedding quality and classification accuracy. Experimental results show that the proposed model achieves superior results with an accuracy rate of 0.9961 compared to state-of-the-art approaches such as ensemble learning and standalone GAT. This achievement highlights the framework’s ability to utilize contextual information for malware detection. The real-world dataset used provides a benchmark for future work, and this research lays a comprehensive foundation for advancing graph-based malware analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cybersecurity: Challenges and Opportunities)
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