TimeGAN: A Novel Solution to Imbalanced Encrypted Traffic Datasets

被引:0
作者
Liu, Hao [1 ]
Zeng, Yong [1 ]
Zhou, Tianci [1 ]
Liu, Zhihong [1 ]
Ma, Jianfeng [1 ]
机构
[1] Xidian Univ, Xian 710126, Shaanxi, Peoples R China
来源
FRONTIERS IN CYBER SECURITY, FCS 2023 | 2024年 / 1992卷
关键词
Encrypted traffic; Imbalanced dataset; Dilated convolution; GAN;
D O I
10.1007/978-981-99-9331-4_31
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Currently, the performance of machine learning-based encrypted traffic recognition models is always unsatisfactory on imbalanced datasets. Existing methods neglected the time series features in the traffic. To solve this problem, this paper proposes TimeGAN, an encrypted traffic time series feature generation model based on dilated convolutional network. Our model not only adopts the advantages of generative adversarial networks (GANs) to model the distribution of time series features of encrypted traffic, but also adopts the structure of dilated convolutional network, which can characterize the causal sequence relationship of traffic sending behavior and generate traffic time series feature data that is closest to the real distribution. We evaluated the performance of the model on three public datasets, and the results showed that our model outperformed all existing models.
引用
收藏
页码:472 / 486
页数:15
相关论文
共 20 条
[11]   Learning from Imbalanced Data for Encrypted Traffic Identification Problem [J].
Ly Vu ;
Dong Van Tra ;
Quang Uy Nguyen .
PROCEEDINGS OF THE SEVENTH SYMPOSIUM ON INFORMATION AND COMMUNICATION TECHNOLOGY (SOICT 2016), 2016, :147-152
[12]  
MITCHELL T, 1989, ANNU REV COMPUT SCI, V4, P417
[13]  
Sychugov A., 2021, AIP C P, V2402
[14]   PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN [J].
Wang, Pan ;
Li, Shuhang ;
Ye, Feng ;
Wang, Zixuan ;
Zhang, Moxuan .
ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2020,
[15]   基于堆栈式自动编码器的加密流量识别方法 [J].
王攀 ;
陈雪娇 .
计算机工程, 2018, 44 (11) :140-147+153
[16]   FLOWGAN:Unbalanced network encrypted traffic identification method based on GAN [J].
Wang, ZiXuan ;
Wang, Pan ;
Zhou, Xiaokang ;
Li, ShuHang ;
Zhang, MoXuan .
2019 IEEE INTL CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, BIG DATA & CLOUD COMPUTING, SUSTAINABLE COMPUTING & COMMUNICATIONS, SOCIAL COMPUTING & NETWORKING (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2019), 2019, :975-983
[17]   A hybrid deep learning based traffic flow prediction method and its understanding [J].
Wu, Yuankai ;
Tan, Huachun ;
Qin, Lingqiao ;
Ran, Bin ;
Jiang, Zhuxi .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2018, 90 :166-180
[18]  
Xuejiao C., 2016, Telecommun. Sci., V31
[19]   PCCN: Parallel Cross Convolutional Neural Network for Abnormal Network Traffic Flows Detection in Multi-Class Imbalanced Network Traffic Flows [J].
Zhang, Yong ;
Chen, Xu ;
Guo, Da ;
Song, Mei ;
Teng, Yinglei ;
Wang, Xiaojuan .
IEEE ACCESS, 2019, 7 :119904-119916
[20]  
Zhengzhi Tang, 2020, International Journal of High Performance Computing and Networking, V16, P26, DOI 10.1504/IJHPCN.2020.110252