Demo Data from EDBT 2026 / reviewers in the wild / expert
Pei-Jie Chang
dblp:428/3636
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 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.
| Theoretical computer science
1 paper |
Quantum computing and quantum information · 100% | |
| Network and information security
1 paper |
Authentication and access control · 62% Network security · 38% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Authentication and access control › authentication
message authentication |
1.0 | 1 | 2026 | Tamper-Proof Quantum Communication Network Enhanced by Machine Learning · IEEE J. Sel. Areas Commun. 2026 |
Quantum computing and quantum information
quantum communication |
1.0 | 1 | 2026 | Tamper-Proof Quantum Communication Network Enhanced by Machine Learning · IEEE J. Sel. Areas Commun. 2026 |
Quantum computing and quantum information
quantum network |
1.0 | 1 | 2026 | Tamper-Proof Quantum Communication Network Enhanced by Machine Learning · IEEE J. Sel. Areas Commun. 2026 |
Network security › intrusion detection and prevention › intrusion detection › attack detection
machine learning-based detection |
0.3 | 1 | 2026 | Tamper-Proof Quantum Communication Network Enhanced by Machine Learning · IEEE J. Sel. Areas Commun. 2026 |
Network security › intrusion detection and prevention › intrusion detection › network intrusion detection
malicious node detection |
0.3 | 1 | 2026 | Tamper-Proof Quantum Communication Network Enhanced by Machine Learning · IEEE J. Sel. Areas Commun. 2026 |
Methods — techniques the papers use, named apart from their topics
unsupervised learning · 2.0supervised learning · 2.0simulation · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Tamper-Proof Quantum Communication Network Enhanced by Machine LearningabstractQuantum cryptography provides a feasible approach to achieving information-theoretic security in point-to-point communication. In contrast to point-to-point protocols, end-to-end secure quantum communication is particularly promising for large-scale quantum networks. Here, we propose a scheme for end-to-end secure communication with private message authentication. For practical purposes, we demonstrate the scheme in a quantum communication network with optical fibers or free-space links that integrate secure repeaters. Furthermore, we present a route proposal model for routing private messages in a quantum network based on the scheme. As messages are routed through a network, malicious nodes may tamper with the messages passing through them with some probability. Based on authentication results, we use machine learning methods to detect malicious nodes, including supervised and unsupervised algorithms. The simulation results in specific networks show that our methods can effectively detect malicious nodes under different tamper probabilities. Yuan-Bin Cheng, Ze-Zhou Sun, Pei-Jie Chang, Gui-Lu Long 0001 |
IEEE J. Sel. Areas Commun. | 3 |