Bin Li

Orcid: 0000-0003-0876-2694

Affiliations:
  • National University of Defense Technology, College of Computer, Science and Technology on Parallel and Distributed Processing Laboratory, Changsha, China


According to our database1, Bin Li authored at least 10 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2025
Density-aware and Cluster-based Federated Anomaly Detection on Data Streams.
Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

2024
Adaptive and augmented active anomaly detection on dynamic network traffic streams.
Frontiers Inf. Technol. Electron. Eng., March, 2024

2023
Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
DFAID: Density-aware and feature-deviated active intrusion detection over network traffic streams.
Comput. Secur., 2022

DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams.
Proceedings of the 28th IEEE International Conference on Parallel and Distributed Systems, 2022

2021
Fden: Mining Effective Information of Features in Detecting Network Anomalies.
Proceedings of the IEEE International Conference on Acoustics, 2021

2020
Outlier Detection Ensemble with Embedded Feature Selection.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
FAAD: an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream.
Frontiers Inf. Technol. Electron. Eng., 2019

2018
FPPB: A Fast and Privacy-Preserving Method Based on the Permissioned Blockchain for Fair Transactions in Sharing Economy.
Proceedings of the 17th IEEE International Conference On Trust, 2018

RZKPB: A Privacy-Preserving Blockchain-Based Fair Transaction Method for Sharing Economy.
Proceedings of the 17th IEEE International Conference On Trust, 2018


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