A systematic review on machine learning and deep learning models for electronic information security in mobile networks
Today's advancements in wireless communication technologies have resulted in a
tremendous volume of data being generated. Most of our information is part of a widespread …
tremendous volume of data being generated. Most of our information is part of a widespread …
Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective
G Kornaros - IEEE Access, 2022 - ieeexplore.ieee.org
As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart
devices act in concert, rarely requiring human intervention, posing significant challenges in …
devices act in concert, rarely requiring human intervention, posing significant challenges in …
Applications of machine learning in resource management for RAN-slicing in 5G and beyond networks: A survey
One of the key foundations of 5th Generation (5G) and beyond 5G (B5G) networks is
network slicing, in which the network is partitioned into several separated logical networks …
network slicing, in which the network is partitioned into several separated logical networks …
ST-BFL: A structured transparency empowered cross-silo federated learning on the blockchain framework
Federated Learning (FL) relies on on-device training to avoid the migration of devices' data
to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios …
to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios …
On the impact of deep neural network calibration on adaptive edge offloading for image classification
Edge devices can offload deep neural network (DNN) inference to the cloud to overcome
energy or processing constraints. Nevertheless, offloading adds communication delay …
energy or processing constraints. Nevertheless, offloading adds communication delay …
Towards edge computing using early-exit convolutional neural networks
In computer vision applications, mobile devices can transfer the inference of Convolutional
Neural Networks (CNNs) to the cloud due to their computational restrictions. Nevertheless …
Neural Networks (CNNs) to the cloud due to their computational restrictions. Nevertheless …
Network slicing in virtualized 5G Core with VNF sharing
Through multiplexing separate virtual networks on the same network infrastructure, network
slicing will lead to customization, scalability, flexibility, and isolation of services in different …
slicing will lead to customization, scalability, flexibility, and isolation of services in different …
Predictive data analytics for electricity fraud detection using tuned cnn ensembler in smart grid
In the smart grid (SG), user consumption data are increasing very rapidly. Some users
consume electricity legally, while others steal it. Electricity theft causes significant damage to …
consume electricity legally, while others steal it. Electricity theft causes significant damage to …
SafeCoder: A machine-learning-based encoding system to embed safety identification information into QR codes
In social networks, the Internet of Things, mobile computing, electronic commerce, and other
fields, Quick Response (QR) codes have been widely used as the interface between online …
fields, Quick Response (QR) codes have been widely used as the interface between online …
Streaming traffic classification: a hybrid deep learning and big data approach
M Seydali, F Khunjush, J Dogani - Cluster Computing, 2024 - Springer
Massive amounts of real-time streaming network data are generated quickly because of the
exponential growth of applications. Analyzing patterns in generated flow traffic streaming …
exponential growth of applications. Analyzing patterns in generated flow traffic streaming …