Zobrazeno 1 - 10
of 1 836
pro vyhledávání: '"LIU Yuling"'
Autor:
LIU Qixu, XIAO Juxin, TAN Yaokang, WANG Chengchun, HUANG Hao, ZHANG Fangjiao, YIN Jie, LIU Yuling
Publikováno v:
Tongxin xuebao, Vol 45, Pp 221-237 (2024)
To gain an in-depth awareness of the application of traffic analysis technology in the industrial Internet, the differences between the industrial Internet and the traditional Internet through the five core traffic analysis processes were illustrated
Externí odkaz:
https://doaj.org/article/6a03f99790f24231acbbd581d2976108
Autor:
Wu, Weiheng, Qiao, Wei, Yan, Wenhao, Jiang, Bo, Liu, Yuling, Liu, Baoxu, Lu, Zhigang, Liu, JunRong
Advanced Persistent Threats (APTs) are continuously evolving, leveraging their stealthiness and persistence to put increasing pressure on current provenance-based Intrusion Detection Systems (IDS). This evolution exposes several critical issues: (1)
Externí odkaz:
http://arxiv.org/abs/2411.02775
Publikováno v:
ACS Omega, Vol 5, Iss 40, Pp 26101-26109 (2020)
Externí odkaz:
https://doaj.org/article/f8d615743c8548a7b35ff0e6a9e5c6d4
Publikováno v:
网络与信息安全学报, Vol 6, Iss 4, Pp 140-147 (2020)
Existing gray visual secret sharing (GVSS) convert the original image into black-white binary image for processing, which causes the pixel information loss problem. For this purpose, a kind of GVSS based on gray superposition was proposed. For each g
Externí odkaz:
https://doaj.org/article/7dd3d2484cdf48e69956b54e9676389d
AI-generated content has accelerated the topic of media synthesis, particularly Deepfake, which can manipulate our portraits for positive or malicious purposes. Before releasing these threatening face images, one promising forensics solution is the i
Externí odkaz:
http://arxiv.org/abs/2404.17867
Autor:
Dong, Cong, Yang, Jiahai, Li, Yun, Wu, Yue, Chen, Yufan, Li, Chenglong, Jiao, Haoran, Yin, Xia, Liu, Yuling
In recent years, DNS over Encrypted (DoE) methods have been regarded as a novel trend within the realm of the DNS ecosystem. In these DoE methods, DNS over HTTPS (DoH) provides encryption to protect data confidentiality while providing better obfusca
Externí odkaz:
http://arxiv.org/abs/2403.12363
Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic
Externí odkaz:
http://arxiv.org/abs/2307.09002
Autor:
Wu, Xianchuan1 (AUTHOR), Liu, Yuling1 (AUTHOR), Xing, Mingjing1 (AUTHOR), Yang, Chun2 (AUTHOR), Hong, Shaoyong1 (AUTHOR) shy2002021@163.com
Publikováno v:
Scientific Reports. 10/15/2024, Vol. 14 Issue 1, p1-15. 15p.
Autor:
Li, Jing, Bowman, John P., Liu, Dejun, He, Yunchuan, Chen, Xiaoyong, Liu, Yuling, He, Zhifei, Iqra, Yang, Jixia
Publikováno v:
In LWT 1 November 2024 211
Publikováno v:
In Journal of Environmental Management November 2024 370