Techniques and Frameworks to Detect and Mitigate Insider Attacks
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 8842
Special Issue Editors
Interests: security; privacy; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: cybersecurity; adversarial machine learning; cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Traditionally, the word “security” in the technology industry was synonymous with addressing attacks that originate externally. However, we are noticing a rise in concerns about attacks that originate internally. Such attacks are known as insider attacks. There is a need to address this problem of growing security attacks from insiders. As a result, it is our goal to explore the state-of-the-art research dealing with new surveys, policies, tools, techniques, concepts, and applications concerning the detection, response and recovery, mitigation, and prevention of insider attacks.
Topics of interest include, but are not limited to:
- Insider attack modeling and attack vectors;
- Implications of insider attacks;
- Policies and regulations to prevent insider attacks;
- Authentication and authorization techniques to address insider attacks;
- Behavioral analytics and fraud detection;
- Data governance and differential privacy to mitigate data leaks;
- Insider attack recovery mechanisms;
- Insider attack datasets;
- Applications of machine learning to detect and prevent insider
Dr. Santosh Aditham
Prof. Dr. Sokratis Katsikas
Guest Editors
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Keywords
- attack vectors
- security
- intrusion detection
- fraud detection
- machine learning
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