Fudong Wu

dblp:328/4503 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0007-7929-7212ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 1 first-author · 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
Cryptographic protocols and secure computation · 61% Blockchain and cryptocurrency security · 30% Network security · 9%
Software engineering, system software, and programming languages
1 paper
Software testing · 100%

Topics — the 5 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Blockchain and cryptocurrency security
blockchain network security
1.012026
Network-Layer Differential Fuzzing for Ethereum · IEEE Trans. Inf. Forensics Secur. 2026
Cryptographic protocols and secure computation › proof systems › zero-knowledge proofs › zero-knowledge set membership proof
range proof
1.012026
sfSpectra: Interval-Agnostic Vector Range Argument for Unstructured Range Assertions · EUROCRYPT (7) 2026
Cryptographic protocols and secure computation › proof systems
zero-knowledge proofs
1.012026
sfSpectra: Interval-Agnostic Vector Range Argument for Unstructured Range Assertions · EUROCRYPT (7) 2026
Software testing › fuzzing
differential fuzzing
1.012026
Network-Layer Differential Fuzzing for Ethereum · IEEE Trans. Inf. Forensics Secur. 2026
Software testing
fuzzing
1.012026
Network-Layer Differential Fuzzing for Ethereum · IEEE Trans. Inf. Forensics Secur. 2026

Methods — techniques the papers use, named apart from their topics

query-based fuzzing · 2.0multi-node differential checking · 2.0
YearPublicationVenuePosition
2026 sfSpectra: Interval-Agnostic Vector Range Argument for Unstructured Range Assertions
Qianhong Wu, Fudong Wu, Zhenyang Ding, Zhiguo Wan
EUROCRYPT (7)4
2026 Network-Layer Differential Fuzzing for Ethereum
abstract
In Ethereum, DevP2P is the fundamental network-layer protocol set that supports consensus mechanisms, transaction propagation and smart contract execution. Due to the importance of DevP2P, its bugs can be exploited by the attacker to cause security problems like denial of service, leading to property loss on Ethereum. However, existing blockchain testing approaches focus on the bug detection of consensus and application layers, causing many serious DevP2P bugs to be missed. In fact, detecting DevP2P bugs has some key challenges, including how to generate effective inputs and how to detect complex bugs. This paper designs D2PFuzz, the first network-layer differential fuzzing approach of bug detection for Ethereum. It consists of two key techniques: (1) aquery-based fuzzing strategythat dynamically generates valid DevP2P messages according to network, chain and node state changes; and (2) amulti-node differential checking methodthat identifies important differences of DevP2P response messages from multiple nodes in the same blockchain to detect semantic bugs. We have evaluated D2PFuzz on five open-source and popular Ethereum node implementations, including Geth, Erigon, Reth, Besu and Nethermind. D2PFuzz in total finds 15 unique bugs, 12 of which are previously unknown. Compared to two state-of-the-art blockchain testing approaches including LOKI and Hive, D2PFuzz improves testing coverage by 3.7x and 21.6x, respectively, and finds 13 bugs missed by these approaches.
Fudong Wu, Qianhong Wu, Jia-Ju Bai, Zhenyu Guan 0002, Willy Susilo
IEEE Trans. Inf. Forensics Secur.1