Deep reinforcement learning in the advanced cybersecurity threat detection and protection

M Sewak, SK Sahay, H Rathore - Information Systems Frontiers, 2023 - Springer
The cybersecurity threat landscape has lately become overly complex. Threat actors
leverage weaknesses in the network and endpoint security in a very coordinated manner to …

Machine learning applications: The past and current research trend in diverse industries

O Ameri Sianaki, A Yousefi, AR Tabesh, M Mahdavi - Inventions, 2019 - mdpi.com
Dramatic changes in the way we collect and process data has facilitated the emergence of a
new era by providing customised services and products precisely based on the needs of …

Joint resource allocation and computation offloading in mobile edge computing for SDN based wireless networks

N Kiran, C Pan, S Wang, C Yin - Journal of Communications …, 2019 - ieeexplore.ieee.org
The rapid growth of the internet usage and the distributed computing resources of edge
devices create a necessity to have a reasonable controller to ensure efficient utilization of …

A survey on how network simulators serve reinforcement learning in wireless networks

S Ergun, I Sammour, G Chalhoub - Computer Networks, 2023 - Elsevier
Rapid adoption of mobile devices, coupled with the increase in prominence of mobile
applications and services, resulted in unprecedented infrastructure requirements for mobile …

mobile-env: An open platform for reinforcement learning in wireless mobile networks

S Schneider, S Werner, R Khalili… - NOMS 2022-2022 …, 2022 - ieeexplore.ieee.org
Recent reinforcement learning approaches for continuous control in wireless mobile
networks have shown impressive results. But due to the lack of open and compatible …

Management and orchestration of virtual network functions via deep reinforcement learning

JSP Roig, DM Gutierrez-Estevez… - IEEE Journal on …, 2019 - ieeexplore.ieee.org
Management and orchestration (MANO) of resources by virtual network functions (VNFs)
represents one of the key challenges towards a fully virtualized network architecture as …

A novel multi-step Q-learning method to improve data efficiency for deep reinforcement learning

Y Yuan, ZL Yu, Z Gu, Y Yeboah, W Wei, X Deng… - Knowledge-Based …, 2019 - Elsevier
Deep reinforcement learning (DRL) algorithms with experience replays have been used to
solve many sequential learning problems. However, in practice, DRL algorithms still suffer …