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- W3104480275 abstract "The purpose of this work was to get acquainted with wireless communication technologies and the information security challenges they create from the viewpoint of 5G and its preceding technologies. 5th Generation (5G) is becoming a global phenomenon and it is currently being implemented in dozens of countries around the globe with it comes new information security challenges. Potential solutions for the challenges are also offered. The outcome of this research is an overview of information security challenges in 5G using Low-power wireless personal area Network (LPWAN) and in the technologies preceding 5G. Possible information security solutions are presented in this work for the new technologies coming with 5G. This work showed that the new technologies coming with 5G, such as the virtualization of hardware and services as well as the utilization of cloud computing, create completely new areas of attack for networks. With this knowledge, Labelled and Freely Available Dataset from Open-Source Repository will be used and it is possible to prevent attacks targeting networks by implementing necessary information security elements. For the training, testing and validation of our dataset which is an IoT and cyber-security based dataset, a well-known MATLAB R2019a software was used for this purpose. The proposed reinforcement learning algorithm for Securing 5G network is designed for mesh topology from the ground up by the model of the network itself using low power personal area networks. We model the network operating in a finite area with a finite number of nodes distributed inside the area randomly in this algorithm. Hence, we defined the service area of the target network by assuming the finiteness of the network in the model." @default.
- W3104480275 created "2020-11-23" @default.
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- W3104480275 date "2020-10-22" @default.
- W3104480275 modified "2023-10-16" @default.
- W3104480275 title "Securing 5G Network using low power wireless personal area network" @default.
- W3104480275 cites W2014084212 @default.
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- W3104480275 doi "https://doi.org/10.1109/ismsit50672.2020.9255054" @default.
- W3104480275 hasPublicationYear "2020" @default.
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