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Applied Sciences
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Artificial Intelligence and Cybersecurity: Challenges and Opportunities
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Artificial Intelligence and Cybersecurity: Challenges and Opportunities

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 1 August 2025 | Viewed by 1726

Special Issue Editors

Dr. Emil Pricop

E-Mail Website
Guest Editor
Automatic Control, Computers & Electronics Department, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania
Interests: cybersecurity; industrial control system security; personal identification methods; Industry 4.0 technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Khalil El-Khatib

E-Mail Website
Guest Editor
1. Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
2. Institute for Cybersecurity and Resilient Systems (ICRS), Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada
Interests: cybersecurity; resilient systems; security and privacy issues in WSN; smart grid security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly transforming the cybersecurity landscape, presenting unprecedented opportunities and significant challenges. This Special Issue will explore this evolving relationship, delving into the latest research, innovations, and critical analyses at the intersection of AI and cybersecurity.

We welcome submissions that cover a broad range of topics, including, but not limited to, the following:

  • AI-based threat detection and response;
  • AI for security analytics and threat intelligence;
  • AI in malware detection and analysis;
  • Automated vulnerability assessment and penetration testing using AI;
  • AI for risk assessment and compliance management in cybersecurity;
  • Adversarial machine learning in cybersecurity;
  • Robustness and reliability of AI-based cybersecurity systems;
  • Privacy and ethical considerations in AI-driven cybersecurity.

We expect this Special Issue to provide a timely and significant platform for researchers and practitioners to present their latest findings and foster collaboration in this rapidly evolving field. We look forward to receiving your contributions.

Dr. Emil Pricop
Dr. Khalil El-Khatib
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

  • artificial intelligence
  • cybersecurity
  • AI-based cybersecurity
  • AI-based threat detection
  • threat intelligence
  • AI-based malware detection
  • adversarial machine learning
  • adversarial artificial intelligence
  • robust and reliable AI-based cybersecurity

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Published Papers (1 paper)

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Research

27 pages, 16238 KiB  
Article
Windows Malware Detection via Enhanced Graph Representations with Node2Vec and Graph Attention Network
by Nisa Vuran Sarı, Mehmet Acı and Çiğdem İnan Acı
Appl. Sci. 2025, 15(9), 4775; https://doi.org/10.3390/app15094775 - 25 Apr 2025
Abstract
As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, [...] Read more.
As malware has become increasingly complex, advanced techniques have emerged to improve traditional detection systems. The increasing complexity of malware poses significant challenges in cybersecurity due to the inability of existing methods to understand detailed and contextual relationships in modern software behavior. Therefore, developing innovative detection frameworks that can effectively analyze and interpret these complex patterns has become critical. This work presents a novel framework integrating API call sequences and DLL information into a unified, graph-based representation to analyze malware behavior comprehensively. The proposed model generates initial embeddings using Node2Vec, which uses a random walk approach to understand structural relationships between nodes. Graph Attention Network (GAT) then enhances these initial embeddings, which utilizes attention mechanisms to incorporate contextual dependencies and enhance semantic representations. Finally, the enhanced embeddings are classified using Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU)s, a custom hybrid CNN-GRU-3 deep learning-based model capable of effectively modeling sequential patterns. The dual role of GAT as a classifier and feature extractor is also analyzed to evaluate its impact on embedding quality and classification accuracy. Experimental results show that the proposed model achieves superior results with an accuracy rate of 0.9961 compared to state-of-the-art approaches such as ensemble learning and standalone GAT. This achievement highlights the framework’s ability to utilize contextual information for malware detection. The real-world dataset used provides a benchmark for future work, and this research lays a comprehensive foundation for advancing graph-based malware analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence and Cybersecurity: Challenges and Opportunities)
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