Zulong Diao

Orcid: 0000-0001-6581-7511

According to our database1, Zulong Diao authored at least 14 papers between 2019 and 2023.

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Bibliography

2023
Improving the Scalability of Distributed Network Emulations: An Algorithmic Perspective.
IEEE Trans. Netw. Serv. Manag., December, 2023

Cognition: Accurate and Consistent Linear Log Parsing Using Template Correction.
J. Comput. Sci. Technol., September, 2023

A lightweight deep learning framework for botnet detecting at the IoT edge.
Comput. Secur., June, 2023

EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks.
Comput. Networks, April, 2023

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection.
Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023

Network Flow Based IoT Anomaly Detection Using Graph Neural Network.
Proceedings of the Knowledge Science, Engineering and Management, 2023

2022
LogDAC: A Universal Efficient Parser-based Log Compression Approach.
Proceedings of the IEEE International Conference on Communications, 2022

2021
NeVe: A Log-based Fast Incremental Network Feature Embedding Approach.
Proceedings of the IEEE Symposium on Computers and Communications, 2021

CyCo: A Temporal Cycle Consistency Based Labeling Method for Time Series Data.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
Quick and Accurate False Data Detection in Mobile Crowd Sensing.
IEEE/ACM Trans. Netw., 2020

Precise and Adaptable: Leveraging Deep Reinforcement Learning for GAP-based Multipath Scheduler.
Proceedings of the 2020 IFIP Networking Conference, 2020

2019
A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems.
IEEE Trans. Intell. Transp. Syst., 2019

Quick and Accurate False Data Detection in Mobile Crowd Sensing.
Proceedings of the 2019 IEEE Conference on Computer Communications, 2019

Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019


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