Cargando…
Deep Reinforcement Learning for Attacking Wireless Sensor Networks
Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows havin...
Autores principales: | Parras, Juan, Hüttenrauch, Maximilian, Zazo, Santiago, Neumann, Gerhard |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231574/ https://www.ncbi.nlm.nih.gov/pubmed/34204726 http://dx.doi.org/10.3390/s21124060 |
Ejemplares similares
-
Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
por: Parras, Juan, et al.
Publicado: (2018) -
Repeated Game Analysis of a CSMA/CA Network under a Backoff Attack
por: Parras, Juan, et al.
Publicado: (2019) -
Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances
por: Parras, Juan, et al.
Publicado: (2021) -
Deep reinforcement learning for wireless networks
por: Yu, F Richard, et al.
Publicado: (2019) -
Simulation of Attacks for Security in Wireless Sensor Network
por: Diaz, Alvaro, et al.
Publicado: (2016)