How to disturb network reconnaissance: a moving target defense approach based on deep reinforcement learning
With the explosive growth of Internet traffic, large sensitive and valuable information is at risk
of cyber attacks, which are mostly preceded by network reconnaissance. A moving target …
of cyber attacks, which are mostly preceded by network reconnaissance. A moving target …
Self-learning multi-objective service coordination using deep reinforcement learning
S Schneider, R Khalili, A Manzoor… - … on Network and …, 2021 - ieeexplore.ieee.org
Modern services consist of interconnected components, eg, microservices in a service mesh
or machine learning functions in a pipeline. These services can scale and run across …
or machine learning functions in a pipeline. These services can scale and run across …
Multi-agent deep reinforcement learning for coordinated multipoint in mobile networks
Macrodiversity is a key technique to increase the capacity of mobile networks. It can be
realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple …
realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple …
Towards attack-resistant service function chain migration: A model-based adaptive proximal policy optimization approach
Network function virtualization (NFV) supports the rapid development of service function
chain (SFC), which efficiently connects a sequence of network virtual function instances …
chain (SFC), which efficiently connects a sequence of network virtual function instances …
AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement Learning
Steerable cameras that can be controlled via a network, to retrieve telemetries of interest
have become popular. In this paper, we develop a framework called AcTrak, to automate a …
have become popular. In this paper, we develop a framework called AcTrak, to automate a …
Knowledge Collaboration-Based Resource Allocation in 6G IoT: A Graph Attention RL Approach
Z Huang, FR Yu, J Cai - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
In future 6G-enabled Internet of Things (IoT), users and devices will be divided into
numerous distributed domains with smaller base station coverage due to the utilization of …
numerous distributed domains with smaller base station coverage due to the utilization of …
Topology-Aware Self-Adaptive Resource Provisioning for Microservices
Microservice architecture is a popular technology for deploying services in cloud computing,
with benefits like loose coupling, high fault tolerance, and scalability. The heterogeneous …
with benefits like loose coupling, high fault tolerance, and scalability. The heterogeneous …
Joint Optimization of Microservice Deployment and Routing in Edge via Multi-Objective Deep Reinforcement Learning
M Hu, H Wang, X Xu, J He, Y Hu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Edge computing technologies with container-based microservice architectures promise to
provide stable and low-latency services for large-scale and complex edge applications …
provide stable and low-latency services for large-scale and complex edge applications …
Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing
Graph-based environments pose unique challenges to multi-agent reinforcement learning.
In decentralized approaches, agents operate within a given graph and make decisions …
In decentralized approaches, agents operate within a given graph and make decisions …
Dimensioning resources of Network Slices for energy-performance trade-off
Within network slicing, Virtual Network Embedding has been vastly studied, ie, deciding in
which physical nodes and links to place virtual functions and links. However, the …
which physical nodes and links to place virtual functions and links. However, the …