[1]
ZHANG L, CUI Y and LIU J, et al 2018(9) Application of machine learning in cyberspace security research J. Journal of Computer 1943-75.
Google Scholar
[2]
WANG W et al 2018 Deep learning for network traffic classification and anomaly detection[D] Hefei University of Science and Technology of China.
Google Scholar
[3]
ANDERSON B and MCGREW D 2016 Identifying encrypted malware traffic with contextual flow data[C] ACM Workshop on Artificial Intelligence & Security 36-41.
DOI: 10.1145/2996758.2996768
Google Scholar
[4]
ANDERSON B and MCGREW D 2017 Machine learning for encrypted malware traffic classification: accounting for noisy labels and non-stationarity[C] The 23rd ACM SIGKDD International Conference 1725-29.
DOI: 10.1145/3097983.3098163
Google Scholar
[5]
WANG L, FENG H M, and LIU B et al 2019 SSL VPN encrypted traffic identification based on hybrid method[J] Computer Applications and Software 321-328.
Google Scholar
[6]
LU G, GUO R H and ZHOU Y et al 2018 Review of malicious traffic feature extration[J]. Netinfo Security 7-15.
Google Scholar
[7]
WANG K 2002 A research on MD5[J]. Chinese Information 78-81.
Google Scholar
[8]
SHIRAVI A, SHIRACI H and TAVALLAEE M et al 2012 Toward developing a systematic approach to generate benchmark datasets for intrusion detection[J] Computers & Security 357-374.
DOI: 10.1016/j.cose.2011.12.012
Google Scholar
[9]
LASHKARI A H, DRAPER-GIL G and MAMUN M S I et al 2016 Characterization of encrypted and VPN traffic using time-related features[C] International Conference on Information Systems Security & Privacy 407-414.
DOI: 10.5220/0005740704070414
Google Scholar
[10]
LUO Z M, XU S B and LIU X D 2020 Scheme for identifying malware traffic with TLS data based on machine learning[J] Chinese Journal of Network and Information Security 77-83.
Google Scholar
[11]
LIU M and WU Z X 2018 Theory and application of support vector machine[J]. Science and Technology Vision 73-74.
Google Scholar
[12]
BREIMAN L 2001 Random forest[J]. Machine Learning 1-33.
Google Scholar
[13]
CHEN T and GUESTRIN C 2016 XGBoost: a scalable tree boosting system[C] The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
DOI: 10.1145/2939672.2939785
Google Scholar
[14]
W. Wang, M. Zhu, J. Wang, X. Zeng and Z. Yang 2017 End-to-end encrypted traffic classification with one-dimensional convolution neural networks 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), Beijing pp.43-48,.
DOI: 10.1109/isi.2017.8004872
Google Scholar
[15]
Wang W, Zhu M, Zeng X W, Ye X Z and Sheng Y Q 2017 Malware traffic classification using convolutional neural network for representation learning 2017 International Conference on Information Networking (ICOIN) pp.712-717,.
DOI: 10.1109/icoin.2017.7899588
Google Scholar
[16]
Tavallaee M, Bagheri E, Lu W and Ghorbani A 2009 A detailed analysis of the KDD CUP 99 data set Proc. 2009 IEEE Int. Conf. Comput. Intell. Security Defense Appl pp.53-58.
DOI: 10.1109/cisda.2009.5356528
Google Scholar
[17]
Wang Z The Applications of Deep Learning on Traffic Identification https://goo.gl/WouIM6.
Google Scholar
[18]
Dainotti, Pescape A and Claffy A 2012 Issues and future directions in traffic classification Network IEEE vol. 26 no. 1 pp.35-40.
DOI: 10.1109/mnet.2012.6135854
Google Scholar
[19]
Creech G and Hu J 2013 Generation of a new ids test dataset: Time to retire the kdd collection Wireless Communications and Networking Conference (WCNC) 2013 IEEE pp.4487-4492.
DOI: 10.1109/wcnc.2013.6555301
Google Scholar
[20]
Mielczarek W and Mon T 2015 USB Data Capture and Analysis in Windows Using USBPcap and Wireshark 431-443.
DOI: 10.1007/978-3-319-19419-6_41
Google Scholar
[21]
CTU University, The Stratosphere IPS Project Dataset, 2016 https://stratosphereips.org/category/dataset.html.
Google Scholar
[22]
Koukis D, Antonatos D, Antoniades D, Markatos E P and Trimintzios P 2006 A Generic Anonymization Framework for Network Traffic 2006 IEEE International Conference on Communications, Istanbul pp.2302-09.
DOI: 10.1109/icc.2006.255113
Google Scholar
[23]
Yang C et al 2019 A malicious traffic detection method based on an SMOTE algorithm and ensemble learning CN110572382A.
Google Scholar