Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Geyi Sheng"'
Publikováno v:
IEEE Access, Vol 7, Pp 8483-8494 (2019)
Wireless body area networks that support fast-growing healthcare applications have to control the access of the sensors in the dynamic network and channel states. In this paper, we propose a sensor access control scheme based on reinforcement learnin
Externí odkaz:
https://doaj.org/article/b9c185d0beac45199fddf603543f151c
Publikováno v:
IEEE Transactions on Communications. 67:6994-7005
The inherent broadcast characteristics of the visible light communication (VLC) channel makes VLC downlinks susceptible to unauthorized terminals in many actual VLC scenarios, such as offices and shopping centers. This paper considers a multiple-inpu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d65cf0902c227ebe651d6cae7b42e19
https://doi.org/10.1109/tcomm.2019.2930247
https://doi.org/10.1109/tcomm.2019.2930247
Publikováno v:
IEEE Access, Vol 7, Pp 8483-8494 (2019)
Wireless body area networks that support fast-growing healthcare applications have to control the access of the sensors in the dynamic network and channel states. In this paper, we propose a sensor access control scheme based on reinforcement learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5eb6eed38463bf8767df51f1156efe0
https://ieeexplore.ieee.org/document/8590699/
https://ieeexplore.ieee.org/document/8590699/
Publikováno v:
IEEE Communications Letters. 23:60-63
In this letter, we propose a physical (PHY)-layer authentication framework to detect spoofing attacks in underwater sensor networks. This scheme exploits the power delay profile of the underwater acoustic channel to discriminate the sensors and appli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3b4523923d1e3e5745ed6521ab0cf33
https://doi.org/10.1109/lcomm.2018.2877317
https://doi.org/10.1109/lcomm.2018.2877317
Publikováno v:
Journal of Communications and Information Networks. 3:39-48
Estates, especially those of public securityrelated companies and institutes, have to protect their privacy from adversary unmanned aerial vehicles (UAVs). In this paper, we propose a reinforcement learning-based control framework to prevent unauthor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6b54e86b4068d725fac1b678cf373bd
https://doi.org/10.1007/s41650-018-0029-y
https://doi.org/10.1007/s41650-018-0029-y
Publikováno v:
Machine Learning for Cyber Security ISBN: 9783030306182
ML4CS
ML4CS
Unmanned aerial vehicles (UAVs) are vulnerable to jamming attacks that aim to interrupt the communications between the UAVs and ground nodes and to prevent the UAVs from completing their sensing duties. In this paper, we design a reinforcement learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::208c27f6a1e8898b4cccdb5ed95dd1f1
https://doi.org/10.1007/978-3-030-30619-9_24
https://doi.org/10.1007/978-3-030-30619-9_24
Publikováno v:
GLOBECOM
Cloud-based malware detection improves the detection performance for mobile devices that offload their malware detection tasks to security servers with much larger malware database and powerful computational resources. In this paper, we investigate t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d431fd65147f3b10580b478cba0734c
https://doi.org/10.1109/glocom.2017.8254503
https://doi.org/10.1109/glocom.2017.8254503