Search Results for author:
Found 4 papers, 1 papers with code
Federated Deep Learning for Intrusion Detection in IoT Networks
The comparison shows that the heterogeneous nature of the data has a considerable negative impact on the model's performance when trained in a distributed manner.
Hierarchical and Decentralised Federated Learning
Hierarchical Federated Learning is likely to be a key enabler for a wide range of applications, such as smart farming and smart energy management, as it can improve performance and reduce costs, whilst also enabling FL workflows to be deployed in environments that are not well-suited to traditional FL.
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration.
An Intrusion Detection System based on Deep Belief Networks
The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach.
Ranked #3 on
Network Intrusion Detection
on CICIDS2017
