Vulnerability Analysis and Adversarial Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 6986
Special Issue Editors
Interests: binary rewriting; malware detection
Special Issue Information
Dear Colleagues,
Traditionally, vulnerabilities come from unreasonable software design, non-standard programming etc. Recently, the advancement of vulnerability analysis in intelligent systems, especially in machine learning algorithms, has been gaining more and more attention.
The vulnerabilities of machine-learning algorithms and models are the foundation of adversarial learning, which is a novel research area that lies at the intersection of machine learning and computer security. Adversarial learning aims at gaining a deeper understanding of the security properties of current machine-learning algorithms against carefully targeted attacks, and at developing suitable countermeasures for the design of more secure learning algorithms.
This Special Issue focuses on the vulnerability analysis of information systems, especially intelligent systems. This includes:
- Information system vulnerability analysis;
- Vulnerability analysis theory and methods;
- Machine learning for vulnerability analysis;
- Vulnerability analysis of AI algorithms, models, systems;
- Formal theory for adversarial leaning;
- Evaluation metrics for adversarial learning;
- Program and binary analysis;
- Trustworthy machine learning and AI;
- Privacy-preserving machine learning.
Dr. Zhi Wang
Dr. Wanpeng Li
Guest Editors
Manuscript Submission Information
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Keywords
- vulnerability analysis
- adversarial learning
- program analysis
- trustworthy AI
- privacy-preserving AI
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