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...

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Bibliographic Details
Main Authors: Juan Parras, Maximilian Hüttenrauch, Santiago Zazo, Gerhard Neumann
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/12/4060