Abstract:
UASNs are used in offshore exploration, auxiliary navigation, and marine military. Underwater sensor nodes’ communication, computation, and storage limitations preclude traditional security mechanisms. Trust models have been investigated as effective tools for UASN security recently.
However, existing trust models lack flexible trust update rules, especially when faced with underwater dynamic fluctuations and a wide range of potential attack modes. This study proposes a reinforcement learning-based UASN trust update mechanism (TUMRL).
Three phases develop the scheme. First, an environment model quantifies underwater fluctuations in sensor data to update trust scores. Then, key degree is defined. In trust update, nodes with higher key degree react more sensitively to malicious attacks, protecting important nodes in the network.
Finally, a reinforcement learning-based trust update mechanism withstands changing attack modes and efficiently updates trust. Our trust update efficiency and network security scheme performs well in experiments.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
