杨焱青｜新疆大学｜学者主页

2026

基于知识蒸馏的轻量级车载网络入侵检测方法

董佳飞，杨焱青，王瀚磊，张琳琳，赵楷

《物联网技术》

2026

Backdoor Defense via Anomaly Sample Isolation with Feature Suppression

Zixian Zhu, Yanqing Yang, Yurong Qian, L H Zhang, Kai Zhao

Lecture notes in computer science

DOI

2026

Backdoor Defense via Proactive Triggering and γ-Suppression Fine-Pruning

Yanqing Yang, Zixian Zhu, Yurong Qian, Linlin Zhang, Kai Zhao

Lecture notes in computer science

DOI

2026

Network Intrusion Detection Method Based on Multi-scale Feature Clustering and Improved Honey Badger Algorithm

H. Wang, Yanqing Yang, Jiafei Dong

Lecture notes in computer science

DOI

2025

非独立同分布与长尾分布下的联邦学习优化方法

冯文举，杨焱青，贾振红，张琳琳

《新疆大学学报(自然科学版中英文)》

DOI

2025

Provenance graph-based advanced persistent threats detection via self-supervised contrastive learning

XuTao Xiang, Yanqing Yang, Yurong Qian, Kai Zhao, Linlin Zhang

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

DOI

2024

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection

Wuxin Tian, Yanping Shen, Na Guo, Jing Yuan, Yanqing Yang

Sensors

DOI

2024

A Fair Contribution Measurement Method for Federated Learning

Peng Guo, Yanqing Yang, Wei Guo, Yanping Shen

Sensors

DOI

2022

CBA-CLSVE: A Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection

Yanping Shen, Kangfeng Zheng, Yanqing Yang, Shuai Liu, Meng Huang

Applied Sciences

DOI

2021

基于深度生成模型的网络攻击检测技术研究

杨焱青

《北京邮电大学》

2021

CENTIME: A Direct Comprehensive Traffic Features Extraction for Encrypted Traffic Classification

Maonan Wang, Zheng Kangfeng, Xinyi Ning, Yanqing Yang, Wang Xiujuan

IEEE International Conference on Computer and Communication Systems (ICCCS)

DOI

2020

An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders

Maonan Wang, Kangfeng Zheng, Dan Luo, Yanqing Yang, Xiujuan Wang

IEEE International Conference on Computer and Communications (ICCC)

DOI

2020

An Explainable Machine Learning Framework for Intrusion Detection Systems

Maonan Wang, Kangfeng Zheng, Yanqing Yang, Xiujuan Wang

IEEE Access

DOI

2020

Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization

Yanqing Yang, Kangfeng Zheng, Bin Wu, Yixian Yang, Xiujuan Wang

IEEE Access

DOI

2019

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Yanqing Yang, Kangfeng Zheng, Chunhua Wu, Yixian Yang

Sensors

DOI

2019

Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks

Yanqing Yang, Kangfeng Zheng, Chunhua Wu, Xinxin Niu, Yixian Yang

Applied Sciences

DOI

杨焱青

精选作品

期刊论文

基于知识蒸馏的轻量级车载网络入侵检测方法

Backdoor Defense via Anomaly Sample Isolation with Feature Suppression

Backdoor Defense via Proactive Triggering and γ-Suppression Fine-Pruning

Network Intrusion Detection Method Based on Multi-scale Feature Clustering and Improved Honey Badger Algorithm

非独立同分布与长尾分布下的联邦学习优化方法

Provenance graph-based advanced persistent threats detection via self-supervised contrastive learning

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection

A Fair Contribution Measurement Method for Federated Learning

CBA-CLSVE: A Class-Level Soft-Voting Ensemble Based on the Chaos Bat Algorithm for Intrusion Detection

基于深度生成模型的网络攻击检测技术研究

CENTIME: A Direct Comprehensive Traffic Features Extraction for Encrypted Traffic Classification

An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders

An Explainable Machine Learning Framework for Intrusion Detection Systems

Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization

Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network

Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks

研究方向