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Zhaoquan Gu

4 papers hereh-index 6175 citations53 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CR2
  • cs.CV1
  • cs.DB1
same name
  • Zhaoquan Gu — 15 papers, h 28
  • Zhaoquan Gu — 4 papers, h 4
  • Zhaoquan Gu — 1 paper, h 2
  • Zhaoquan Gu — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedGRIDAI: Generating and Repairing Intrusion Detection Rules via Collaboration among Multiple LLM-based Agents

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CR2025★ 1 cited

GRIDAI: Generating and Repairing Intrusion Detection Rules via Collaboration among Multiple LLM-based Agents

Jiarui Li, Yuhan Chai, Lei Du +3

Rule-based network intrusion detection systems play a crucial role in the real-time detection of Web attacks. However, most existing works primarily focus on automatically generati…

cs.DB2025

Temporal k-Core Query, Revisited

Yinyu Liu, Kaiqiang Yu, Shengxin Liu +2

Querying cohesive subgraphs in temporal graphs is essential for understanding the dynamic structure of real-world networks, such as evolving communities in social platforms, shifti…

cs.CR2025

Reversible Data Hiding over Encrypted Images via Intrinsic Correlation in Block-Based Secret Sharing

Jianhui Zou, Weijia Cao, Shuang Yi +2

With the rapid advancements in information technology, reversible data hiding over encrypted images (RDH-EI) has become essential for secure image management in cloud services. How…

cs.CV2023

F2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns

Yaguan Qian, Chenyu Zhao, Zhaoquan Gu +5

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as se…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.