Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Feiyi Xie"'
Autor:
Huanhuan Song, Hong Wen, Qicong Yang, Jie Tang, Yi Chen, Tengyue Zhang, Feiyi Xie, Songlin Chen
Publikováno v:
IEEE Access, Vol 9, Pp 27287-27297 (2021)
For real-time industrial wireless edge-enabled cyber-physical systems, this paper proposes an adaptive secure transmission mechanism under the large scale path loss to fight against randomly distributed illegal sensing devices. According to changes i
Externí odkaz:
https://doaj.org/article/b2f18d87d36b45679f837088acaf378f
Autor:
Run-Fa Liao, Hong Wen, Jinsong Wu, Fei Pan, Aidong Xu, Huanhuan Song, Feiyi Xie, Yixin Jiang, Minggui Cao
Publikováno v:
IEEE Access, Vol 7, Pp 116390-116401 (2019)
In this paper, we investigate the security threats in mobile edge computing (MEC) of Internet of things, and propose a deep-learning (DL)-based physical (PHY) layer authentication scheme which exploits channel state information (CSI) to enhance the s
Externí odkaz:
https://doaj.org/article/cb958868c9a842479ebf3d7948770375
Publikováno v:
Sensors, Vol 19, Iss 11, p 2440 (2019)
In this paper, a deep learning (DL)-based physical (PHY) layer authentication framework is proposed to enhance the security of industrial wireless sensor networks (IWSNs). Three algorithms, the deep neural network (DNN)-based sensor nodes’ authenti
Externí odkaz:
https://doaj.org/article/04b49887ae384310b24627b1a48a016e
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:2796-2806
Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a pre-processing platform for the massive data generated by the terminals and guarantee lower delay and more se
Publikováno v:
IEEE Transactions on Industrial Informatics. 17:4999-5009
The cooperative physical layer security scheme can enhance the communication security of industrial wireless nodes that are computing and energy limitation, which provides the lightweight security for future industrial wireless networks (IWNs). By ai
Autor:
Jie Tang, Hong Wen, Tengyue Zhang, Qicong Yang, Huanhuan Song, Feiyi Xie, Songlin Chen, Yi Chen
Publikováno v:
IEEE Access, Vol 9, Pp 27287-27297 (2021)
For real-time industrial wireless edge-enabled cyber-physical systems, this paper proposes an adaptive secure transmission mechanism under the large scale path loss to fight against randomly distributed illegal sensing devices. According to changes i
Publikováno v:
IEEE Transactions on Vehicular Technology. 69:7596-7608
Due to the requirement of real time controlling on the vehicular ad-hoc networks (VANETs), the security measure should be fast, lightweight, and strong. In this paper, we investigate the secure communication between the on-board unit and the roadside
Autor:
Huanhuan Song, Tengyue Zhang, Wenjing Hou, Jiang Yixin, Hong Wen, Jinsong Wu, Run-Fa Liao, Feiyi Xie
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence. 4:276-286
Radio frequency fingerprinting (RFF) is a lightweight access authentication method for the mobile terminal nodes. The classification accuracy of RFF is effectively improved by machine learning, of which the performance is deeply enhanced with the inc
Publikováno v:
IEEE Internet of Things Journal. 7:2077-2088
Unlike most of the upper layer authentication mechanisms, the physical (PHY) layer authentication takes advantages of channel impulse response from wireless propagation to identify transmitted packages with low-resource consumption, and machine learn
Autor:
Minggui Cao, Run-Fa Liao, Jiang Yixin, Fei Pan, Feiyi Xie, Huanhuan Song, Aidong Xu, Jinsong Wu, Hong Wen
Publikováno v:
IEEE Access, Vol 7, Pp 116390-116401 (2019)
In this paper, we investigate the security threats in mobile edge computing (MEC) of Internet of things, and propose a deep-learning (DL)-based physical (PHY) layer authentication scheme which exploits channel state information (CSI) to enhance the s