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 March 2025 | Viewed by 140
Special Issue Editors
Interests: machine mearning; behavioral biometrics; image classification; cyber security; deep learning
Interests: cyber security; NLP; secure software engineering; IoT security; machine learning
Interests: cyber security; digital forensics; cyberawareness; information security; cyber situational awareness; computer networking security; machine learning
Special Issues, Collections and Topics in MDPI journals
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
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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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