Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

no code implementations • 22 Dec 2023 • Taha Eghtesad, Sirui Li, Yevgeniy Vorobeychik, Aron Laszka

The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors.

Multi-agent Reinforcement Learning

Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense

no code implementations • 27 Nov 2019 • Taha Eghtesad, Yevgeniy Vorobeychik, Aron Laszka

In this paper, we propose a multi-agent partially-observable Markov Decision Process model of MTD and formulate a two-player general-sum game between the adversary and the defender.

Multi-agent Reinforcement Learning reinforcement-learning +1