Kaidong Wu

dblp:203/8761 · DBLP profile ↗
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5ranked-venue papers
1as first author
3since 2021 · last 2023
0000-0001-9818-6592ORCID · corroborated

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

Security and privacy · 2 · 2 since 2021Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1
YearPublicationVenuePosition
2023 HorusEye: A Realtime IoT Malicious Traffic Detection Framework using Programmable Switches
Yutao Dong, Qing Li 0006, Kaidong Wu, Ruoyu Li 0003, Dan Zhao 0003, Gareth Tyson, Junkun Peng, Yong Jiang 0001, Shutao Xia, Mingwei Xu 0001
USENIX Security Symposium3
2021 BDLedger: A Scalable Distributed Ledger for Large-Scale Data Recording
Gang Huang 0001, Kaidong Wu, Chaoran Luo, Huaqian Cai, Xiang Jing, Yun Ma 0002
BlockSys2
2021 A first look at blockchain-based decentralized applications
abstract
Summary With the increasing popularity of blockchain technologies in recent years, blockchain‐based decentralized applications (DApps for short in this paper) have been rapidly developed and widely adopted in many areas, being a hot topic in both academia and industry. Despite of the importance of DApps, we still have quite little understanding of DApps along with its ecosystem. To bridge the knowledge gap, this paper presents the first comprehensive empirical study of blockchain‐based DApps to date, based on an extensive dataset of 995 Ethereum DApps and 29,846,075 transaction logs over them. We make a descriptive analysis of the popularity of DApps, summarize the patterns of how DApps use smart contracts to access the underlying blockchain, and explore the worth‐addressing issues of deploying and operating DApps. Based on the findings, we propose some implications for DApp users to select proper DApps, for DApp developers to improve the efficiency of DApps, and for blockchain vendors to enhance the support of DApps.
Kaidong Wu, Yun Ma 0002, Gang Huang 0001, Xuanzhe Liu
Softw. Pract. Exp.1
2019 Software-Defined Infrastructure for Decentralized Data Lifecycle Governance: Principled Design and Open Challenges
abstract
Exploring and mining the explosive burst of "big data" has already generated a lot of innovative applications, especially the recent advances of AI applications, and thus produced big values to the human society and civilization. However, due to the centralized patterns of data governance activities, including creation, sharing, exchange, management, analytics, tracing, and accounting, the potential values of big data distributed on the Internet are far away from being adequately explored. The recent announcement of data protection policies/laws such as GDPR makes the problem even more challenging. We are now at a moment of truth where the data governance infrastructure should be reconsidered and redesigned. In this paper, we propose a software-defined infrastructure design in a decentralized fashion: data owners are able to implement and deploy their own rules to the application systems where the data are produced for further governance activities. Such a fashion is quite similar to the popular software-defined networking where users are allowed to deploy rules of switches and customize the use. Our principled infrastructure design can radically reform the current data governance activities into a decentralized topology. On the one hand, data can be separated from the application that generates the data, and data owners can have the full rights to decide where their data should be stored and how the data can be shared. On the other hand, data users can search, discover, integrate, and analyze the data from various data sources according to their application requirements and scenarios. As a result, we argue that our infrastructure can establish a new generation of responsive decentralized data governance that can promote the innovation of linking data to better adapt the open environment and diverse user requirements. With this perspective, we briefly discuss some key insights and enumerate several related new technologies and open challenges.
Gang Huang 0001, Chaoran Luo, Kaidong Wu, Yun Ma 0002, Ying Zhang 0012, Xuanzhe Liu
ICDCS3
2018 LogPruner: detect, analyze and prune logging calls in Android apps
Xin Zhou 0008, Kaidong Wu, Huaqian Cai, Shuai Lou, Ying Zhang 0012, Gang Huang 0001
Sci. China Inf. Sci.2