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Yu Chen
Researcher at Binghamton University
Publications - 244
Citations - 4872
Yu Chen is an academic researcher from Binghamton University. The author has contributed to research in topics: Cloud computing & Edge computing. The author has an hindex of 34, co-authored 217 publications receiving 3896 citations. Previous affiliations of Yu Chen include State University of New York System & University of Southern California.
Papers
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Journal ArticleDOI
Collaborative Detection of DDoS Attacks over Multiple Network Domains
Yu Chen,Kai Hwang,Wei-Shinn Ku +2 more
TL;DR: This paper develops a distributed change-point detection (DCD) architecture using change aggregation trees (CAT), and proves that this DDoS defense system can scale well to cover 84 AS domains, wide enough to safeguard most ISP core networks from real-life DDoS flooding attacks.
Journal ArticleDOI
Collaborative detection and filtering of shrew DDoS attacks using spectral analysis
TL;DR: A new spectral template-matching approach to countering shrew distributed denial-of-service (DDoS) attacks by spectral analysis against pre-stored template of average attack spectral characteristics, suitable for either software or hardware implementation.
Proceedings ArticleDOI
Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier
TL;DR: The result is encouraging in that the accuracy of NBC is improved and approaches 82% when the dataset size increases and it is demonstrated that NBC is able to scale up to analyze the sentiment of millions movie reviews with increasing throughput.
Proceedings ArticleDOI
Dynamic Urban Surveillance Video Stream Processing Using Fog Computing
TL;DR: A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision making and the potential to enable multi-target tracking function using a simpler single target tracking algorithm is explored.
Proceedings ArticleDOI
Malicious node detection in wireless sensor networks using weighted trust evaluation
TL;DR: This paper proposed a novel scheme based on weighted-trust evaluation to detect malicious nodes of a hierarchical WSN architecture that can reduce the communication overhead between sensor nodes by utilizing clustered topology.