TY - GEN
T1 - Attribute Based Federated-Reinforcement Learning Approach for Drone Authorization
AU - Rao, K. Rajesh
AU - Pradhan, Tribikram
AU - Krishna Prakasha, K.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Drones have become a vital part of the technology-driven world we live in today. From using it for delivering our day-to-day items to high grade military purposes, drones have spread their demands to various sectors of the economy. However, a little exploration has been done when it comes to talking about their security. Security generally comprises two aspects: authentication and authorization. While authentication is more to do with confirming the users with who they say they are, authorization is when the user’s permissions to access a resource is examined and a decision to approve or deny the access is taken by the system. This paper discusses authorization in detail using reinforcement learning and federated learning. The model is trained on a synthetic dataset, and is evaluated based on progressive validation loss (PVL), F1-Score, and a new metric called Permit Score.
AB - Drones have become a vital part of the technology-driven world we live in today. From using it for delivering our day-to-day items to high grade military purposes, drones have spread their demands to various sectors of the economy. However, a little exploration has been done when it comes to talking about their security. Security generally comprises two aspects: authentication and authorization. While authentication is more to do with confirming the users with who they say they are, authorization is when the user’s permissions to access a resource is examined and a decision to approve or deny the access is taken by the system. This paper discusses authorization in detail using reinforcement learning and federated learning. The model is trained on a synthetic dataset, and is evaluated based on progressive validation loss (PVL), F1-Score, and a new metric called Permit Score.
UR - https://www.scopus.com/pages/publications/105000818671
UR - https://www.scopus.com/pages/publications/105000818671#tab=citedBy
U2 - 10.1007/978-3-031-31723-1_2
DO - 10.1007/978-3-031-31723-1_2
M3 - Conference contribution
AN - SCOPUS:105000818671
SN - 9783031317224
T3 - Communications in Computer and Information Science
SP - 14
EP - 28
BT - Machine Intelligence and Smart Systems - 3rd International Conference, MISS 2023, Revised Selected Papers
A2 - Gupta, Manish
A2 - Agrawal, Shikha
A2 - Gupta, Kamlesh
A2 - Agrawal, Jitendra
A2 - Cengis, Korhan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Machine Intelligence and Smart Systems, MISS 2023
Y2 - 24 January 2023 through 25 January 2023
ER -