Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuhong Gui
dblp:133/3871
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Computer networks · 1
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Energy systems and smart grids › power grid security
smart grid security |
0.2 | 1 | 2013 | A novel method to detect bad data injection attack in smart grid · INFOCOM 2013 |
Energy systems and smart grids › power system monitoring
state estimation |
0.0 | 1 | 2013 | A novel method to detect bad data injection attack in smart grid · INFOCOM 2013 |
Methods — techniques the papers use, named apart from their topics
state estimation · 0.2adaptive partitioning · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Abnormal traffic-indexed state estimation: A cyber-physical fusion approach for Smart Grid attack detection
Ting Liu 0002, Yanan Sun 0002, Yang Liu 0090, Yuhong Gui, Dai Wang, Chao Shen 0001 |
Future Gener. Comput. Syst. | 4 |
| 2013 | A novel method to detect bad data injection attack in smart gridabstractBad data injection is one of most dangerous attacks in smart grid, as it might lead to energy theft on the end users and device breakdown on the power generation. The attackers can construct the bad data evading the bad data detection mechanisms in power system. In this paper, a novel method, named as Adaptive Partitioning State Estimation (APSE), is proposed to detect bad data injection attack. The basic ideas are: 1) the large system is divided into several subsystems to improve the sensitivity of bad data detection; 2) the detection results are applied to guide the subsystem updating and re-partitioning to locate the bad data. Two attack cases are constructed to inject bad data into an IEEE 39-bus system, evading the traditional bad data detection mechanism. The experiments demonstrate that all bad data can be detected and located within a small area using APSE. Ting Liu 0002, Yun Gu, Dai Wang, Yuhong Gui, Xiaohong Guan |
INFOCOM | 4 |