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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) belonging to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 19227

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 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-anonymized 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 (3 papers)

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Research

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31 pages, 3690 KB  
Article
A Study on Improving the Automatic Classification Performance of Cybersecurity MITRE ATT&CK Tactics Using NLP-Based ModernBERT and BERTopic Models
by Jaehwan Baek, Jeonghoon O, Seungwoo Jeong and Wooju Kim
Electronics 2025, 14(22), 4434; https://doi.org/10.3390/electronics14224434 - 13 Nov 2025
Cited by 3 | Viewed by 2305
Abstract
Cyber Threat Intelligence (CTI) reports are essential resources for identifying the Tactics, Techniques, and Procedures (TTPs) of hackers and cyber threat actors. However, these reports are often lengthy and unstructured, which limits their suitability for automatic mapping to the MITRE ATT&CK framework. This [...] Read more.
Cyber Threat Intelligence (CTI) reports are essential resources for identifying the Tactics, Techniques, and Procedures (TTPs) of hackers and cyber threat actors. However, these reports are often lengthy and unstructured, which limits their suitability for automatic mapping to the MITRE ATT&CK framework. This study designs and compares five hybrid classification models that combine statistical features (TF-IDF), transformer-based contextual embeddings (BERT and ModernBERT), and topic-level representations (BERTopic) to automatically classify CTI reports into 12 ATT&CK tactic categories. Experiments using the rcATT dataset, consisting of 1490 public threat reports, show that the model integrating TF-IDF and ModernBERT achieved a micro-precision of 72.25%, reflecting a 10.07-percentage-point improvement in detection precision compared with the baseline. The model combining TF-IDF and BERTopic achieved a micro F0.5 of 67.14% and a macro F0.5 of 63.20%, demonstrating balanced performance across both frequent and rare tactic classes. These findings indicate that integrating statistical, contextual, and semantic representations can improve the balance between precision and recall while enabling clearer interpretation of model outputs in multi-label CTI classification. Furthermore, the proposed model shows potential applicability for improving detection efficiency and reducing analyst workload in Security Operations Center (SOC) environments. Full article
(This article belongs to the Special Issue Natural Language Processing and Data Science Methods to Mitigate Digital Threats)
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38 pages, 1337 KB  
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
Cited by 24 | Viewed by 6838
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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24 pages, 429 KB  
Systematic Review
Advances in NLP Techniques for Detection of Message-Based Threats in Digital Platforms: A Systematic Review
by José Saias
Electronics 2025, 14(13), 2551; https://doi.org/10.3390/electronics14132551 - 24 Jun 2025
Cited by 4 | Viewed by 8186
Abstract
Users of all ages face risks on social media and messaging platforms. When encountering suspicious messages, legitimate concerns arise about a sender’s malicious intent. This study examines recent advances in Natural Language Processing for detecting message-based threats in digital communication. We conducted a [...] Read more.
Users of all ages face risks on social media and messaging platforms. When encountering suspicious messages, legitimate concerns arise about a sender’s malicious intent. This study examines recent advances in Natural Language Processing for detecting message-based threats in digital communication. We conducted a systematic review following PRISMA guidelines, to address four research questions. After applying a rigorous search and screening pipeline, 30 publications were selected for analysis. Our work assessed the NLP techniques and evaluation methods employed in recent threat detection research, revealing that large language models appear in only 20% of the reviewed works. We further categorized detection input scopes and discussed ethical and privacy implications. The results show that AI ethical aspects are not systematically addressed in the reviewed scientific literature. Full article
(This article belongs to the Special Issue Natural Language Processing and Data Science Methods to Mitigate Digital Threats)
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