Apr 29, 2021 · In this work, we aim to develop two key mechanisms to build secure in-vehicle networks: (1) RL-based proactive defense mechanism to achieve ...

Through extensive simulation experiments, we validate that the proposed robust mDRL algorithm can help the deployed proactive security mechanism achieve both ...

This work proposes DESOLATER, which is a multi-agent deep reinforcement learning (mDRL)-based network slicing technique that can help determine two key ...

May 18, 2021 · In this work, we aim to develop two key mechanisms to build secure in-vehicle networks: (1) RL-based proactive defense mechanism to achieve.

DESOLATER: Deep Reinforcement Learning-Based Resource Allocation and Moving Target Defense Deployment Framework . IEEE Access, (), –. doi:10.1109/access ...

Through extensive simulation experiments, we validate that the proposed robust mDRL algorithm can help the deployed proactive security mechanism achieve both ...

Jul 27, 2021 · Army researchers developed a new machine learning-based framework to enhance the security of computer networks inside vehicles without undermining performance.

Aug 3, 2021 · DESOLATOR -- which stands for deep reinforcement learning-based resource allocation and moving target defense deployment framework – uses ...

AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning (DRL) techniques. AutoPentest-DRL can determine the most ...

Desolater: Deep reinforcement learning-based resource allocation and moving target defense deployment framework. S Yoon, JH Cho, DS Kim, TJ Moore, F Free ...