Demo Data from EDBT 2026 / reviewers in the wild / expert
Shouyin Xu
dblp:378/5501
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0006-7095-600XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › isolation
container isolation |
0.8 | 1 | 2024 | Condo: Enhancing Container Isolation Through Kernel Permission Data Protection · IEEE Trans. Inf. Forensics Secur. 2024 |
Systems and software security
memory safety |
0.8 | 1 | 2024 | Condo: Enhancing Container Isolation Through Kernel Permission Data Protection · IEEE Trans. Inf. Forensics Secur. 2024 |
Systems and software security
operating system security |
0.8 | 1 | 2024 | Condo: Enhancing Container Isolation Through Kernel Permission Data Protection · IEEE Trans. Inf. Forensics Secur. 2024 |
Operating systems › virtualization
OS-level virtualization |
0.2 | 1 | 2024 | Condo: Enhancing Container Isolation Through Kernel Permission Data Protection · IEEE Trans. Inf. Forensics Secur. 2024 |
Operating systems
virtualization |
0.2 | 1 | 2024 | Condo: Enhancing Container Isolation Through Kernel Permission Data Protection · IEEE Trans. Inf. Forensics Secur. 2024 |
Methods — techniques the papers use, named apart from their topics
kernel permission data protection · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Condo: Enhancing Container Isolation Through Kernel Permission Data ProtectionabstractContainer technology is widely adopted due to its features such as light weight and ease of rapid deployment. However, as an OS-level virtualization mechanism, container isolation relies on the kernel’s security mechanisms and the kernel permission data (usually non-control flow data) used by these mechanisms. None of the existing mitigation schemes for non-control flow data attacks provide an effective and practical solution to container security since they either trigger too much overhead, have limited effectiveness over attacks launched in specific ways, or can only be used to protect some specific kernel data. In addition, none of them accurately identify the kernel data associated with container isolation. In this paper, we provide a solution called Condo that enhances container isolation by protecting the associated kernel permission data. We first present a generic non-control flow kernel data protection mechanism that protects different types of kernel data uniformly with low overhead and is not limited by attack methods or data types. We then demystify the models of various kernel access control mechanisms in the container environment, and identify the subject and object permission data that are critical to container isolation. Finally, we provide a solution named Condo to enhance container isolation, which is completely transparent to the existing container ecosystem, including containerized applications and container management/orchestration tools such as Docker. Experimental results show that Condo can effectively reduce the compromises of container isolation due to memory corruption attacks with an acceptable overhead. Shouyin Xu, Yuewu Wang, Lingguang Lei, Kun Sun 0001, Jiwu Jing, Jie Wang 0138 |
IEEE Trans. Inf. Forensics Secur. | 1 |