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Vanitha, N.
- Investigation of Deep Learning Optimizers for False Window Size Injection Attack Detection in Unmanned Aerial Vehicle Network Architecture
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Authors
N. Vanitha
1,
G. Padmavathi
1
Affiliations
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, IN
1 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, IN
Source
ICTACT Journal on Communication Technology, Vol 12, No 3 (2021), Pagination: 2465-2470Abstract
The Unmanned Aerial Vehicle (UAV) network plays a prominent role in this pandemic era. Nowadays UAVs are applied in various applications like military, civil etc. This article works on the Search and Rescue application part. UAV networks are applied in search and rescue operations in order to find the missing people in Hill areas. Due to false data dissemination attacks some UAVs in the network will lost the data so the rescue will become an issue. In order to detect those attacks this work uses Feed Forward Neural network with back propagation algorithm. This work experiments chosen optimizers to get the accurate detection of attack and compares the results among the optimizers All the more explicitly this examination did in the Delay- Tolerant based Decentralized Multi-Layer UAV ad-hoc organization Assisting VANET (DDMUAV) design utilizing Opportunistic Network Environment (ONE) test system.Keywords
Unmanned Aerial Vehicle, Delay Tolerant, Neural Network, Optimizer, Simulation.
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