Machine Learning for Network Security
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 31598
Special Issue Editor
Interests: big data systems; cloud computing; mobile and pervasive computing; security and privacy
Special Issue Information
Dear Colleagues,
As cyberattacks grow in volume and complexity, machine learning has been widely adopted to deal with various cybersecurity attacks and malicious behaviors. On the other hand, machine learning models are vulnerable to data pollution attacks. Enhancing network security and achieving robustness of ML-driven network systems are the critical issues in the development of cyberphysical systems. In this special issue, we invite submissions leveraging the power of machine learning to detect various attacks and understand the vulnerability of network systems, exploring cutting-edge machine learning technologies in the field of network security, and addressing the robustness of machine learning models against imperfect training samples in untrusted cyberphysical systems. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.
Dr. Wenbo He
Guest Editor
Manuscript Submission Information
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Keywords
- network security
- cyberphysical systems
- anomaly detection
- vulnerability analysis
- machine learning
- privacy and security