Demo Data from VLDB 2026 / reviewers in the wild / expert
Jiedong Chen
dblp:352/4020
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0002-9656-7310ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 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
2 papers |
Network security · 100% | |
| Computer networks
1 paper |
Network management and operations · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › intrusion detection and prevention › intrusion detection › attack detection
advanced persistent threat detection |
1.0 | 2 | 2024 | NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation · NDSS 2024 Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools · CCS 2023 |
Network security › intrusion detection and prevention › intrusion detection
endpoint detection and response |
0.7 | 1 | 2023 | Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools · CCS 2023 |
Network security › intrusion detection and prevention
intrusion detection |
0.7 | 1 | 2023 | Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools · CCS 2023 |
Methods — techniques the papers use, named apart from their topics
provenance graph analysis · 1.5systematic measurement · 0.7questionnaire study · 0.7interview study · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation
Shaofei Li, Feng Dong 0008, Xusheng Xiao, Haoyu Wang 0001, Fei Shao, Jiedong Chen, Yao Guo 0001, Xiangqun Chen, Ding Li 0001 |
NDSS | 6 |
| 2023 | Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response ToolsabstractProvenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future Advanced Persistent Threats (APT) defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry (1) overlooking client-side overhead; (2) imbalancedalarm triage cost and interpretation cost; and (3) excessive server side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry. Feng Dong 0008, Shaofei Li, Peng Jiang 0007, Ding Li 0001, Haoyu Wang 0001, Liangyi Huang, Xusheng Xiao, Jiedong Chen, Xiapu Luo, Yao Guo 0001, Xiangqun Chen |
CCS | 8 |