Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Congyuan XU"'
Publikováno v:
Dianxin kexue, Vol 39, Pp 85-100 (2023)
Existing intrusion detection techniques often require numerous malicious samples for model training.However, in real-world scenarios, only a small number of intrusion traffic samples can be obtained, which belong to few-shot scenarios.To address this
Externí odkaz:
https://doaj.org/article/73c7e344b03b40859ee1690315fffcd8
Publikováno v:
IEEE Access, Vol 6, Pp 48697-48707 (2018)
To improve the performance of network intrusion detection systems (IDS), we applied deep learning theory to intrusion detection and developed a deep network model with automatic feature extraction. In this paper, we consider the characteristics of th
Externí odkaz:
https://doaj.org/article/80816f182b7245fd99dde6ca49ca99fe
Publikováno v:
Chemical Speciation & Bioavailability, Vol 28, Iss 1-4, Pp 209-215 (2016)
Carbon and nutrients as well as suspended solids (SS) removal by chemically enhanced primary treatment (CEPT) were conducted in the Qingshan wastewater treatment plant in Huangshi, Hubei Province. Feasibility of this process for wastewater treatment
Externí odkaz:
https://doaj.org/article/61a35f8978664aafa011afbcd188ac4a
Publikováno v:
Journal of Applied Mathematics, Vol 2015 (2015)
The resonant tunneling device (RTD) has attracted much attention because of its unique negative differential resistance characteristic and its functional versatility and is more suitable for implementing the threshold logic gate. The universal logic
Externí odkaz:
https://doaj.org/article/48a721d744424e1db6f043cb1c1c7a45
Publikováno v:
Journal of Information Science & Engineering; May2024, Vol. 40 Issue 3, p551-566, 16p
Autor:
Ziming Zhao, Zhaoxuan Li, Jialun Jiang, Fengyuan Yu, Fan Zhang, Congyuan Xu, Xinjie Zhao, Rui Zhang, Shize Guo
Publikováno v:
IEEE Transactions on Dependable and Secure Computing. :1-18
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::681d2667798290dadc7a13707f8888e0
https://doi.org/10.1109/tdsc.2023.3242134
https://doi.org/10.1109/tdsc.2023.3242134
Publikováno v:
IEEE Transactions on Information Forensics and Security. 15:3540-3552
Conventional intrusion detection systems based on supervised learning techniques require a large number of samples for training, while in some scenarios, such as zero-day attacks, security agencies can only intercept a limited number of shots of mali
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4460b29be94efb1e2bb7e4447a77b4e
https://doi.org/10.1109/tifs.2020.2991876
https://doi.org/10.1109/tifs.2020.2991876
Publikováno v:
Computers & Security. 85:77-88
Botnets have become one of the main threats to cyberspace security currently. More and more bots utilize the domain generation algorithm (DGA) to generate malicious domain names to communicate with Command & Control (CC it only needs to input the dom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f5ba444ac9ef1a86f2afc780c9039fd
https://doi.org/10.1016/j.cose.2019.04.015
https://doi.org/10.1016/j.cose.2019.04.015
Publikováno v:
IEEE Access, Vol 6, Pp 48697-48707 (2018)
To improve the performance of network intrusion detection systems (IDS), we applied deep learning theory to intrusion detection and developed a deep network model with automatic feature extraction. In this paper, we consider the characteristics of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad61ad240598bb737486343b0afc0907
https://doi.org/10.1109/access.2018.2867564
https://doi.org/10.1109/access.2018.2867564
Publikováno v:
Journal of Information Security and Applications. 60:102879
Low-rate Denial of Service (LDoS) attacks use the low-rate requests to achieve the occupation of the network resources and have strong concealment. The traditional signal analysis based detection methods are challenging to detect LDoS attacks in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::651c563bb62ab7a6556c4404d2b6c65e
https://doi.org/10.1016/j.jisa.2021.102879
https://doi.org/10.1016/j.jisa.2021.102879