Segmented federated learning for adaptive intrusion detection system
Cyberattacks are a major issues and it causes organizations great financial, and reputation
harm. However, due to various factors, the current network intrusion detection systems …
harm. However, due to various factors, the current network intrusion detection systems …
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems
Federated learning (FL) is a decentralized learning technique that enables participating
devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) …
devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) …
A review of federated learning in intrusion detection systems for iot
Intrusion detection systems are evolving into intelligent systems that perform data analysis
searching for anomalies in their environment. The development of deep learning …
searching for anomalies in their environment. The development of deep learning …
Federated learning-based network intrusion detection with a feature selection approach
Y Qin, M Kondo - 2021 International conference on electrical …, 2021 - ieeexplore.ieee.org
With the increase and diversity of network attacks, machine learning has shown its efficiency
in realizing intrusion detection. Federated Learning (FL) has been proposed as a new …
in realizing intrusion detection. Federated Learning (FL) has been proposed as a new …
Adaptive intrusion detection in the networking of large-scale lans with segmented federated learning
Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic
patterns based on a handcrafted dataset of rules. Recently, the application of machine …
patterns based on a handcrafted dataset of rules. Recently, the application of machine …
[HTML][HTML] Enhancing privacy-preserving intrusion detection through federated learning
Detecting anomalies, intrusions, and security threats in the network (including Internet of
Things) traffic necessitates the processing of large volumes of sensitive data, which raises …
Things) traffic necessitates the processing of large volumes of sensitive data, which raises …
Resource-Efficient Federated Learning for Network Intrusion Detection
R Doriguzzi-Corin, S Cretti… - 2024 IEEE 10th …, 2024 - ieeexplore.ieee.org
Maintaining up-to-date attack profiles is a critical challenge for Network Intrusion Detection
Systems (NIDSs). State-of-the-art solutions based on Machine Learning (ML) algorithms …
Systems (NIDSs). State-of-the-art solutions based on Machine Learning (ML) algorithms …
FIDS: Detecting DDoS through federated learning based method
J Li, Z Zhang, Y Li, X Guo, H Li - 2021 IEEE 20th International …, 2021 - ieeexplore.ieee.org
Recently, federated learning has been used by Network Intrusion Detection Systems
(NIDSs) to expanding data features while preserving data privacy. However, non …
(NIDSs) to expanding data features while preserving data privacy. However, non …
An efficient federated learning system for network intrusion detection
Network intrusion detection is used to detect unauthorized activities on a digital network,
with which the cybersecurity teams of organizations can then kick-start prevention protocols …
with which the cybersecurity teams of organizations can then kick-start prevention protocols …
CFL-IDS: An Effective Clustered Federated Learning Framework for Industrial Internet of Things Intrusion Detection
Y Shan, Y Yao, X Zhou, T Zhao, B Hu… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The Industrial Internet of Things (IIoT) offers the manufacturing sector opportunities for
transformation and upgrade but also carries significant security risks. Traditional federated …
transformation and upgrade but also carries significant security risks. Traditional federated …