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Natural Language Processing and Data Science Methods to Mitigate Digital...
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Natural Language Processing and Data Science Methods to Mitigate Digital Threats

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 944

Special Issue Editor

Dr. José Saias

E-Mail Website
Guest Editor
1. Department of Informatics, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal
2. Vista Lab, ALGORITMI Centre, University of Évora, 7000-671 Évora, Portugal
Interests: artificial intelligence (question-answering, opinion mining, and persuasion detection in digital content); distributed systems; data security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era where social media has become ingrained in the daily lives of people of all ages and is used to influence public opinion, artificial intelligence plays a crucial role in detecting fake news and digital threats. The proliferation of fake profiles, cyberbullying, manipulative tactics, and other harmful online behaviors has prompted significant attention from researchers. As a result, the scientific community is actively developing methods to detect false information, report harassment and scams, and enhance user safety.

This field encompasses a wide range of tasks, from data security to complex behavioral analysis, social engineering prevention, or content classification. This Special Issue aims to highlight recent advancements in addressing digital threats, leveraging cutting-edge techniques from natural language processing and data science, or other AI-based methods employed on textual and mixed online content.

Relevant topics covered in this Special Issue include, but are not limited to, the following:

  • Hate speech and fake news detection;
  • Online harassment, trolling, and cyberbullying detection;
  • Bait link and other scams detection;
  • Fake social media profile detection.

Dr. José Saias
Guest Editor

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

  • natural language processing
  • cybersecurity
  • misinformation
  • deep learning
  • threat modeling
  • large language models
  • identity theft
  • text forensics
  • fact-checking
  • data science
  • text classification

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Published Papers (1 paper)

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Research

38 pages, 1337 KiB  
Article
Quantum-Enhanced Machine Learning for Cybersecurity: Evaluating Malicious URL Detection
by Lauren Eze, Umair B. Chaudhry and Hamid Jahankhani
Electronics 2025, 14(9), 1827; https://doi.org/10.3390/electronics14091827 - 29 Apr 2025
Abstract
The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and [...] Read more.
The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and scalability remain limited for emerging adversarial approaches. Quantum machine learning (QML) is a transformative strategy that relies on quantum computation and high-dimensional feature spaces to potentially overcome classical computational limitations. However, the accuracy of QML models such as QSVM and QCNN for URL detection in comparison to classical models remains unexplored. This study evaluates ML models (SVMs and CNNs) and QML models (QSVMs and QCNNs) on a dataset, employing data preprocessing techniques such as outliers, feature scaling and feature selection with ANOVA and PCA. Quantum models utilized ZZFeatureMap and ZFeatureMap for data encoding, to transfer original data to qubits. The achieved results showed that CNNs outperformed QCNNs and QSVMs outperformed SVMs in the performance evaluation, demonstrating a competitive potential of quantum computing. QML shows promise for cybersecurity, particularly given the QSVM’s kernel advantages, but current hardware limits the QCNN’s practicality. The significance of this research is to contribute to the growing body of knowledge in cybersecurity by providing a comparative analysis of classical and quantum ML models for classifying malicious URLs. Full article
(This article belongs to the Special Issue Natural Language Processing and Data Science Methods to Mitigate Digital Threats)
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