Deep Reinforcement Learning for User Access Control in UAV Networks

Author(s):  
Yang Cao ◽  
Lin Zhang ◽  
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.


Author(s):  
Saadia Kedjar ◽  
Abdelkamel Tari ◽  
Peter Bertok

With the advancement of web services technology, security has become an increasingly important issue. Various security standards have been developed to secure web services at the transport and message level, but application level has received less attention. The security solutions at the application level focus on access control which cannot alone ensure the confidentiality and integrity of information. The solution proposed in this paper consists on a hybrid model that combines access control (AC) and information flow control (IFC). The AC mechanism uses the concept of roles and attributes to control user access to web services' methods. The IFC mechanism uses labels to control how the roles access to the system's objects and verify the information flows between them to ensure the information confidentiality and integrity. This manuscript describes the model, gives the demonstration of the IFC model safety, presents the modeling and implementation of the model and a case study.


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