Deep Reinforcement Learning For Multi-User Access Control in Non-Terrestrial Networks

Author(s):  
Yang Cao ◽  
Shao-Yu Lien ◽  
Ying-Chang Liang
IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 46600-46611
Author(s):  
Hang Zhou ◽  
Xiaoyan Wang ◽  
Masahiro Umehira ◽  
Xianfu Chen ◽  
Celimuge Wu ◽  
...  

2021 ◽  
Author(s):  
László Viktor Jánoky ◽  
Péter Ekler ◽  
János Levendovszky

JSON Web Tokens (JWT) provide a scalable, distributed way of user access control for modern web-based systems. The main advantage of the scheme is that the tokens are valid by themselves – through the use of digital signing – also imply its greatest weakness. Once issued, there is no trivial way to revoke a JWT token. In our work, we present a novel approach for this revocation problem, overcoming some of the problems of currently used solutions. To compare our solution to the established solutions, we also introduce the mathematical framework of comparison, which we ultimately test using real-world measurements.


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