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Applied Sciences
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Application of Deep Learning for Cybersecurity
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Application of Deep Learning for Cybersecurity

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 661

Special Issue Editors

Dr. Rushit Dave

E-Mail Website
Guest Editor
Computer Information Science Department, Minnesota State University, Mankato, MN 56001, USA
Interests: machine mearning; behavioral biometrics; image classification; cyber security; deep learning
Dr. Mounika Vanamala

E-Mail Website
Guest Editor
Computer Science Department, University of Wisconsin, Eau Claire, WI 54701, USA
Interests: cyber security; NLP; secure software engineering; IoT security; machine learning
Prof. Dr. Mario Antunes

E-Mail Website
Guest Editor
1. School of Technology and Management, Polytechnic University of Leiria, 2411-901 Leiria, Portugal
2. INESC TEC, CRACS, 4200-465 Porto, Portugal
Interests: cybersecurity; digital forensics; cyberawareness; information security; computers networking security; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ki-Hyun Jung

E-Mail Website
Guest Editor
Department of Software Convergence, Andong National University, Andong 36729, Republic of Korea
Interests: cryptography; VLSI; authentication technologies; network security and ubiquitous computing security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is an invitation to the Applied Sciences Special Issue "Application of Deep Learning for Cybersecurity" to explore the deep learning transformation effect in the domain of cybersecurity. Traditional methods of defense are quite inadequate against attack vectors that are very quickly evolving, especially in view of the rapidly growing sophistication of cyber threats. Deep learning, with its ability to interrogate vast amounts of data and identify complex patterns within them, has come up with a very promising solution to such challenges.

We investigate, in this Special Issue, very recent research and applications of deep learning techniques at the service of different dimensions of Cybersecurity, including intrusion detection systems, malware classification, anomaly detection, and threat intelligence. The various contributions presented here illustrate the power of deep learning in helping to resolve some of the most important security issues organizations are facing today.

In particular, we would like to provide an overview of the current status of the field and point out the emerging trends, with the purpose of encouraging discussions about future directions of deep learning applied for cybersecurity. We wish that this set of articles is a useful contribution to all those researchers, practitioners, and decision-makers who need to keep pace with the ever-changing landscape of cyber threats.

Dr. Rushit Dave
Dr. Mounika Vanamala
Prof. Dr. Mario Antunes
Prof. Dr. Ki-Hyun Jung
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

  • deep Learning
  • cybersecurity
  • intrusion detection & malware analysis
  • image and video classification
  • anomaly detection
  • threat intelligence
  • security analytics
  • cyber crime
  • adversarial machine learning
  • phishing emails and spams detection

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Published Papers (1 paper)

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Research

26 pages, 2653 KiB  
Article
Attacker Attribution in Multi-Step and Multi-Adversarial Network Attacks Using Transformer-Based Approach
by Romina Torres and Ana García
Appl. Sci. 2025, 15(15), 8476; https://doi.org/10.3390/app15158476 - 30 Jul 2025
Viewed by 245
Abstract
Recent studies on network intrusion detection using deep learning primarily focus on detecting attacks or classifying attack types, but they often overlook the challenge of attributing each attack to its specific source among many potential adversaries (multi-adversary attribution). This is a critical and [...] Read more.
Recent studies on network intrusion detection using deep learning primarily focus on detecting attacks or classifying attack types, but they often overlook the challenge of attributing each attack to its specific source among many potential adversaries (multi-adversary attribution). This is a critical and underexplored issue in cybersecurity. In this study, we address the problem of attacker attribution in complex, multi-step network attack (MSNA) environments, aiming to identify the responsible attacker (e.g., IP address) for each sequence of security alerts, rather than merely detecting the presence or type of attack. We propose a deep learning approach based on Transformer encoders to classify sequences of network alerts and attribute them to specific attackers among many candidates. Our pipeline includes data preprocessing, exploratory analysis, and robust training/validation using stratified splits and 5-fold cross-validation, all applied to real-world multi-step attack datasets from capture-the-flag (CTF) competitions. We compare the Transformer-based approach with a multilayer perceptron (MLP) baseline to quantify the benefits of advanced architectures. Experiments on this challenging dataset demonstrate that our Transformer model achieves near-perfect accuracy (99.98%) and F1-scores (macro and weighted ≈ 99%) in attack attribution, significantly outperforming the MLP baseline (accuracy 80.62%, macro F1 65.05% and weighted F1 80.48%). The Transformer generalizes robustly across all attacker classes, including those with few samples, as evidenced by per-class metrics and confusion matrices. Our results show that Transformer-based models are highly effective for multi-adversary attack attribution in MSNA, a scenario not or under-addressed in the previous intrusion detection systems (IDS) literature. The adoption of advanced architectures and rigorous validation strategies is essential for reliable attribution in complex and imbalanced environments. Full article
(This article belongs to the Special Issue Application of Deep Learning for Cybersecurity)
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