Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Xueyuan DUAN"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Due to the large computational overhead, underutilization of features, and high bandwidth consumption in traditional SDN environments for DDoS attack detection and mitigation methods, this paper proposes a two-stage detection and mitigation
Externí odkaz:
https://doaj.org/article/27ea8c7e61ee4007aa7a44748b77e1da
Publikováno v:
Tongxin xuebao, Vol 45, Pp 208-226 (2024)
Since software defined network (SDN) was more vulnerable to network attacks than traditional networks, the research progress of abnormal traffic detection for software defined network in recent years from the technical principle and architecture char
Externí odkaz:
https://doaj.org/article/8f2189e29c124943848f86e4956e8b85
Publikováno v:
Tongxin xuebao, Vol 45, Pp 54-67 (2024)
Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features wa
Externí odkaz:
https://doaj.org/article/425fbe0f9a7a4cfcb49e98a2140a1026
Publikováno v:
Tongxin xuebao, Vol 43, Pp 53-64 (2022)
Traditional low-rate denial of service (LDoS) attack detection methods were complex in feature extraction, high in computational cost, single in experimental data background settings, and outdated in attack scenarios, so it was difficult to meet the
Externí odkaz:
https://doaj.org/article/34f01587797e4dc1a88e8dd35c825d7a
Publikováno v:
Tongxin xuebao, Vol 43, Pp 65-76 (2022)
Aiming at the problem that most of the traditional network traffic anomaly detection methods only pay attention to the fine-grained features of traffic data, and make insufficient use of multi-scale feature information, which may lead to low accuracy
Externí odkaz:
https://doaj.org/article/6d11a5145a0a41f7a71dcfff08818f3e
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 11, Iss 1, Pp 1-19 (2022)
Abstract In this paper, a real cloud computing platform-oriented Low-rate Denial of Service (LDoS) attack detection method based on time-frequency characteristics of traffic data is proposed. All the traffic data flowing through the Web server is acq
Externí odkaz:
https://doaj.org/article/3e560ca96cf64c8f875e98fbbbbc4042
Publikováno v:
Tongxin xuebao, Vol 43, Pp 1-13 (2022)
As the deficiency of learning ability of traditional semi-supervised depth anomaly detection model to unbalanced multidimensional data distribution and the difficulty of model training, a multi-dimensional time series anomaly detection method based o
Externí odkaz:
https://doaj.org/article/72ba12079037479a8692542239c87790
Publikováno v:
Computer Communications. 198:206-216
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15f368f611f1ac37ceb4153725888278
https://doi.org/10.1016/j.comcom.2022.10.024
https://doi.org/10.1016/j.comcom.2022.10.024