New Trends in Cybersecurity and Data Privacy

Topic Information

Dear Colleagues,

As cyber threats continue to grow in sophistication, traditional security models are increasingly being pushed to their limits, driving the need for more advanced technological solutions. Key innovations include artificial intelligence for intrusion detection, Zero Trust Architecture for access control, and blockchain to ensure data integrity. A critical area of focus is the integration of privacy-enhancing technologies, designed to protect sensitive information while maintaining usability.

Simultaneously, the emergence of quantum computing presents a formidable challenge to existing cryptographic standards. In response, the field of post-quantum cryptography is rapidly gaining momentum, with research efforts focused on developing algorithms capable of resisting quantum-based attacks and ensuring long-term data security.

The aim of this topic is to contribute to the advancement of cybersecurity and data privacy by bringing together cutting-edge research from both theoretical and applied perspectives. We invite authors to submit original and innovative manuscripts on topics including the following:

• Artificial Intelligence in Cybersecurity;
• Zero Trust Architecture;
• Blockchain and Data Integrity;
• Privacy-Enhancing Technologies;
• Post-Quantum Cryptography;
• Impact of Quantum Computing on Cryptography;
• Regulatory and Legal Aspects of Data Privacy;
• Cybersecurity in the Internet of Things (IoT);
• Cybersecurity in Industrial Control Systems;
• Security and Privacy in Cloud Computing;
• Cybersecurity in Emerging Technologies;
• Advanced Threats and Intrusion Detection Techniques;
• Cybersecurity for Critical Infrastructure;
• AI and Privacy in the Digital Ecosystem;
• Security and Privacy in Mobile and Wearable Technologies;
• Cybersecurity Risk Management and Resilience;
• Cybersecurity in Autonomous and Edge Computing Systems;
• Human-Centric Cybersecurity.

Prof. Dr. Ming Hour Yang
Prof. Dr. Murugesan Vijayalakshmi
Prof. Dr. Selvaraj Mercy Shalinie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	23.1 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Information information	2.9	6.5	2010	20.9 Days	CHF 1800	Submit

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Published Papers (4 papers)

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All Applied Sciences BDCC Electronics Information

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33 pages, 1446 KB  

Open AccessArticle

FMT-SVM: A Communication-Efficient Federated Multi-Task Support Vector Machine Framework for Healthcare

by Naima Firdaus, Sachin Balkrushna Jadhav, Zahid Raza, Maria Lapina and Mikhail Babenko

Big Data Cogn. Comput. 2026, 10(4), 119; https://doi.org/10.3390/bdcc10040119 - 12 Apr 2026

Viewed by 356

Abstract

Federated learning has become a promising paradigm in the training of decentralized machine learning models across clients without sharing raw data, thereby preserving privacy. Current federated support vector machine methods are mainly based on the learning of a single global model, which inadequately [...] Read more.

Federated learning has become a promising paradigm in the training of decentralized machine learning models across clients without sharing raw data, thereby preserving privacy. Current federated support vector machine methods are mainly based on the learning of a single global model, which inadequately addresses the challenges presented by heterogeneous and non-IID client data distributions. To overcome these limitations, we propose FMT-SVM, a novel federated multi-task learning framework that jointly trains both binary and multi-class classification tasks within each client, where the client uses a unified convolutional neural network encoder to extract common features among tasks, which are passed to task-specific linear SVM heads dedicated to each classification task. By leveraging a primal optimization integrating task covariance and global consensus regularization, FMT-SVM explicitly models relationships between heterogeneous tasks and enforces alignment across clients, effectively handling the non-IID nature of data distributions. Unlike traditional FL methods that exchange entire model parameters or large support vector sets, our method communicates only the compact SVM heads during aggregation, greatly reducing communication overhead and enhancing scalability for clients with limited bandwidth. To further enhance privacy, Gaussian differential privacy mechanisms are applied to client updates, balancing privacy preservation with predictive performance. Experiments are performed on two medical image datasets: the Pediatric Pneumonia Dataset and the Breast Ultrasound dataset, demonstrating that the FMT-SVM framework achieves competitive accuracy on both binary and multi-class tasks while maintaining communication efficiency and privacy guarantees. These results highlight the capability of the proposed FMT-SVM framework as a practical, scalable, and privacy-aware solution for the federated true multi-task learning problem in sensitive healthcare applications. Full article

(This article belongs to the Topic New Trends in Cybersecurity and Data Privacy)

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23 pages, 804 KB  

Open AccessArticle

Importance of Cybersecurity Competencies in Higher Education and for Employers

by Marko Kompara, Lili Nemec Zlatolas, Muhamed Turkanović and Marko Hölbl

Appl. Sci. 2026, 16(7), 3260; https://doi.org/10.3390/app16073260 - 27 Mar 2026

Viewed by 417

Abstract

The global shortage of qualified cybersecurity professionals continues to intensify, underscoring the need for targeted and practice-oriented education and training. This study examines and compares the cybersecurity competencies emphasized in higher education with those prioritized by employers. The findings reveal notable discrepancies between [...] Read more.

The global shortage of qualified cybersecurity professionals continues to intensify, underscoring the need for targeted and practice-oriented education and training. This study examines and compares the cybersecurity competencies emphasized in higher education with those prioritized by employers. The findings reveal notable discrepancies between academic and industry expectations. Employers, particularly larger organizations, assign the greatest importance to competencies related to organizational and human security, whereas higher education institutions tend to prioritize technical cybersecurity domains. These insights provide a foundation for designing more comprehensive and industry-aligned cybersecurity curricula and can support the development of educational pathways tailored to specific learner groups and workforce needs. Full article

(This article belongs to the Topic New Trends in Cybersecurity and Data Privacy)

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21 pages, 2310 KB  

Open AccessArticle

Adversarial Perturbations for Defeating Cryptographic Algorithm Identification

by Shuijun Yin, Di Wu, Haolan Zhang, Heng Li, Zhiyuan Yao and Wei Yuan

Big Data Cogn. Comput. 2026, 10(1), 13; https://doi.org/10.3390/bdcc10010013 - 30 Dec 2025

Viewed by 953

Abstract

Recent advances in machine learning have enabled highly effective ciphertext-based cryptographic algorithm identification, posing a potential threat to encrypted communication. Inspired by adversarial example techniques, we present CSPM (Class-Specific Perturbation Mask Generation), a novel adversarial-defense framework that enhances ciphertext unidentifiability through misleading machine-learning-based [...] Read more.

Recent advances in machine learning have enabled highly effective ciphertext-based cryptographic algorithm identification, posing a potential threat to encrypted communication. Inspired by adversarial example techniques, we present CSPM (Class-Specific Perturbation Mask Generation), a novel adversarial-defense framework that enhances ciphertext unidentifiability through misleading machine-learning-based cipher classifiers. CPSM constructs lightweight, reversible bit-level perturbations that alter statistical ciphertext features without affecting legitimate decryption. The method leverages class prototypes to capture representative bit-distribution patterns for each cryptographic algorithm and integrates two complementary mechanisms—mimicry-based perturbing, which steers ciphertexts toward similar cipher classes, and distortion-based perturbing, which disrupts distinctive statistical traits—through a ranking-based greedy search. Extensive experiments on seven widely used cryptographic algorithms and fifteen NIST statistical feature configurations demonstrate that CSPM consistently reduces algorithm-identification accuracy by over 25%. These results confirm that perturbation position selection, rather than magnitude, dominates attack efficacy. CSPM provides a practical defense mechanism, offering a new perspective for safeguarding encrypted communications against statistical and machine-learning-based traffic analysis. Full article

(This article belongs to the Topic New Trends in Cybersecurity and Data Privacy)

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33 pages, 5160 KB  

Open AccessArticle

Embedding-Driven Synthetic Malware Generation with Autoencoders and Cluster-Tangent Diffusion

by Gunnika Kapoor, Sathvika Nadipalli and Fabio Di Troia

Appl. Sci. 2025, 15(21), 11791; https://doi.org/10.3390/app152111791 - 5 Nov 2025

Viewed by 1330

Abstract

Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract. As a result, generating high quality synthetic samples from scarce data sets [...] Read more.

Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract. As a result, generating high quality synthetic samples from scarce data sets becomes a crucial method for strengthening detection software. This paper focuses on presenting generation techniques that optimize the embedding space to produce high-quality synthetic samples, even under constrained datasets. The dataset used in this paper consists of 500 Windows malware API call samples that were processed using embedding and Generative AI (Gen AI) techniques to generate synthetic malware. Two novel contributions are highlighted in this paper. (1) The integration of autoencoders with pretrained NLP models (BERT and ELMo) to enhance the quality of embeddings. Autoencoders extract features and learn patterns from the data to generate higher-quality embeddings than those generated using other techniques alone. (2) Cluster-Tangent Diffusion (CT-Diff): a novel application of manifold diffusion. Manifold diffusion improves upon diffusion and other Gen AI techniques by focusing on generating samples along the distribution of the original data using structured noise instead of standard gaussian noise. Collectively these two contributions have consistently outperformed previous techniques. Furthermore, the results demonstrate the feasibility of generating reliable fake samples even in low data scenarios. Full article

(This article belongs to the Topic New Trends in Cybersecurity and Data Privacy)

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