Deep Learning for Advanced Malware Detection
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".
Deadline for manuscript submissions: 15 February 2026 | Viewed by 118
Special Issue Editors
Interests: cybersecurity; machine learning; data analytics; cloud computing
Interests: mobile and wireless networks security and privacy; VoIP security; IoT security and privacy; DNS security; security education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue explores cutting-edge research in deep learning for enhanced malware detection, a critical area of cybersecurity. Deep learning offers the potential to automatically identify complex malware behavior patterns that are trained on large datasets which can surpass traditional solutions. These models provide a more robust defense, detecting new malware variants and attack vectors with increasing confidence as they process more data. This Special Issue aims to stimulate the development of novel methodologies and solutions, addressing current cybersecurity challenges by focusing on deep learning's role in malware detection. We encourage submissions of original research and review articles presenting novel approaches, significant improvements over existing methods, or insightful reviews identifying key research gaps.
Scope and Topics
This Special Issue invites contributions exploring the following key themes in deep learning for malware detection.
I. Novel Architectures and Techniques
- Novel deep learning architectures, including hybrid approaches integrating with traditional malware analysis;
- Hybrid deep learning models for enhanced feature extraction.
II. Practical Applications
- Cloud-based malware detection solutions;
- Resource-constrained device models;
- Real-time malware detection.
III. Advanced Techniques
- Zero-day malware detection;
- Transfer learning for cross-domain malware detection.
IV. Security and Privacy
- Privacy-preserving malware detection;
- Threat intelligence integration.
V. Network Focus
- Network-based malware detection.
VI. Efficiency Concerns
- Energy-efficient deep learning models.
VII. Emerging Challenges and Future Directions
- Adversarial attacks and defenses in deep learning-based malware detection;
- Explainable AI for malware analysis.
We particularly encourage submissions exploring the intersection of these themes, such as cloud-based zero-day detection for resource-constrained devices.
Expected Impact
This Special Issue anticipates significant contributions to the field of malware detection, including the development of more accurate and efficient detection models, improved zero-day detection capabilities, and practical solutions for diverse platforms and environments. It will serve as a valuable resource for researchers, practitioners, and industry professionals, fostering collaboration and accelerating the advancement of deep learning-driven malware defense.
Target Audience
This Special Issue targets researchers, practitioners, and industry professionals in cybersecurity, machine learning, and related fields.
Dr. Prasanthi Sreekumari
Dr. Georgios Kambourakis
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. Information is an international peer-reviewed open access monthly 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 1600 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
- malware detection
- networks security and privacy
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