Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.0
2.9
Journals
Electronics
Special Issues
Recent Advances of Applied Data Science in Cybersecurity and Process...
electronics-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Electronics Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances of Applied Data Science in Cybersecurity and Process Monitoring

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

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2468

Editors

Dr. Priyadarsi Nanda

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology, Sydney, NSW 2007, Australia
Interests: cybersecurity; networking; distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Manoranjan Mohanty

E-Mail Website
Guest Editor
School of Information Systems, Carnegie Mellon University, Doha 24866, Qatar
Interests: multimedia security and forensics; privacy preservation; social engineering attack mitigation

Special Issue Information

Dear Colleagues,

Applied data science is revolutionizing cybersecurity by providing intelligent, scalable solutions to combat increasingly sophisticated threats. They are primarily driven by artificial intelligence (AI) and machine learning (ML), enabling a shift from reactive to proactive and predictive strategies in both cybersecurity and process monitoring. Key developments include the rise in generative AI, the widespread adoption of deep learning and advanced behavioral analytics. In process monitoring (including manufacturing, healthcare and finance), data science is used to optimize operations, enhance efficiency and ensure quality control.

Focus of the SI: This Special Issue aims to fosters research collaborations between the cybersecurity, AI and computer science communities to explore innovative solutions for real-time threat detection, predictive analytics and threat intelligence, AI driven attack mitigation, anomaly detection in industrial systems, agentic AI and real-time analytics with big data. It aims to address key questions on innovative methods and processes, modern tools and key technologies bringing together synergy between industry and academia.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Real-time threat and anomaly detection
  • Predictive analytics and threat intelligence
  • Automated incident response
  • User and entity behavior analytics
  • AI-driven attack mitigation
  • Optimization and quality control
  • Agentic AI
  • Tear-time analytics using big data

We look forward to receiving your contributions.

Dr. Priyadarsi Nanda
Dr. Manoranjan Mohanty
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 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

  • cybersecurity
  • big data
  • attack mitigation
  • network

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

22 pages, 1794 KB  
Article
Privacy-Preserving Peer-to-Peer Cross-Domain Collaborative Filtering via Intent-Adaptive Graph Reconstruction
by Munan Li, Hao Zhang, Jialong Li and Sinan Chen
Electronics 2026, 15(14), 3121; https://doi.org/10.3390/electronics15143121 - 15 Jul 2026
Viewed by 461
Abstract
Cross-domain collaborative filtering effectively alleviates the data sparsity issue but raises serious privacy concerns. Federated learning has been integrated into cross-domain collaborative filtering to reduce these risks by securely exchanging embeddings or model parameters. However, the current federated paradigm often relies on the [...] Read more.
Cross-domain collaborative filtering effectively alleviates the data sparsity issue but raises serious privacy concerns. Federated learning has been integrated into cross-domain collaborative filtering to reduce these risks by securely exchanging embeddings or model parameters. However, the current federated paradigm often relies on the simple alignment of coarse-grained representations, while propagating information on a rigid local graph. Without fine-grained preference modeling, models easily suffer from representation collapse. The inherent sparsity of local graphs further limits their robustness. To address these issues, we propose P2P-IAGR, a privacy-preserving peer-to-peer cross-domain collaborative filtering framework based on intent-adaptive graph reconstruction. Specifically, our framework first disentangles user and item representations into fine-grained latent intent prototypes. We perturb these prototypes using Local Differential Privacy (LDP) and securely exchange them across domains. A contrastive learning strategy is then used for cross-domain alignment. Next, guided by the combined cross-domain intent prior, P2P-IAGR differentiably reconstructs an augmented graph view. We apply a dual-view structural contrastive learning objective to dynamically inject external collaborative signals into the sparse local topology. Extensive experiments on real-world datasets show that P2P-IAGR significantly outperforms state-of-the-art methods, achieving an average improvement of 5.85% in NDCG and 6.89% in HR. Full article
(This article belongs to the Special Issue Recent Advances of Applied Data Science in Cybersecurity and Process Monitoring)
►▼ Show Figures

Figure 1

28 pages, 1445 KB  
Article
Cost-Aware Lightweight Deep Learning for Intrusion Detection: A Comparative Study on UNSW-NB15 and CIC-IDS2017
by Marija Gombar, Amir Topalović and Mirjana Pejić Bach
Electronics 2026, 15(8), 1603; https://doi.org/10.3390/electronics15081603 - 12 Apr 2026
Cited by 2 | Viewed by 1047
Abstract
Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet [...] Read more.
Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet, a convolutional model optimized for feature-centric detection, and SigNet, a gated recurrent model designed for sequence-oriented modeling of ordered flow-feature representations. Both models are trained with Cost-Robust Focal Loss (CRF-Loss), a cost-aware objective that penalizes false positives and false negatives according to deployment-specific risk preferences. We evaluate the models on the UNSW-NB15 and CIC-IDS2017 benchmarks using six standard metrics (accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC)), complemented by an analysis of false-positive behavior. On CIC-IDS2017, ForNet achieves precision up to 0.95 and MCC up to 0.93 with AUROC above 0.94, while SigNet shows a stronger recall-oriented profile on UNSW-NB15. In an ablation study, replacing Binary Cross-Entropy with CRF-Loss reduces the false-positive rate by approximately 15–20% and improves robustness-oriented metrics such as MCC by up to 12% on CIC-IDS2017. Rather than claiming universal state-of-the-art performance, the study focuses on performance–risk trade-offs under realistic operational constraints. The results highlight how architectural bias and cost-aware optimisation jointly shape IDS behaviour and offer benchmark-based guidance for interpreting performance–risk trade-offs in lightweight intrusion detection. Full article
(This article belongs to the Special Issue Recent Advances of Applied Data Science in Cybersecurity and Process Monitoring)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

28 pages, 1120 KB  
Review
Artificial Intelligence and Machine Learning Methods in Recidivism Prediction: A Thematic Literature Review
by Marwah Alrushaid and Areej Alhogail
Electronics 2026, 15(14), 3107; https://doi.org/10.3390/electronics15143107 - 15 Jul 2026
Viewed by 549
Abstract
Recidivism remains a persistent challenge in criminal justice systems because data are fragmented across jurisdictions, privacy laws are stringent, and legal definitions of recidivism vary. This thematic literature review synthesizes evidence on risk factors influencing recidivism, traditional and centralized artificial intelligence and machine [...] Read more.
Recidivism remains a persistent challenge in criminal justice systems because data are fragmented across jurisdictions, privacy laws are stringent, and legal definitions of recidivism vary. This thematic literature review synthesizes evidence on risk factors influencing recidivism, traditional and centralized artificial intelligence and machine learning (AI/ML) approaches, explainable AI (XAI) for transparency and bias auditing, generative AI for local data augmentation, and federated learning in privacy-sensitive environments. The review identifies recurring patterns, including improved ensemble performance on balanced datasets and increased use of XAI for bias auditing, while also highlighting persistent limitations related to methodological variation, privacy constraints, jurisdictional heterogeneity, and fairness concerns. Based on these findings, Recid-Fed is proposed as a conceptual research agenda to guide future research on privacy-preserving and jurisdiction-aware recidivism prediction. Rather than presenting an implemented system, this paper consolidates the current literature and outlines the key research requirements that should be addressed in future framework development and empirical validation studies. Full article
(This article belongs to the Special Issue Recent Advances of Applied Data Science in Cybersecurity and Process Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop