The rapid proliferation of Internet of Things (IoT) devices across multiple
sectors has escalated serious network security concerns. This has prompted
ongoing research in Machine Learning (ML)-based Intrusion Detection Systems
(IDSs) for cyber-attack classification. Traditional ML models require data
transmission from IoT devices to a centralized server for traffic analysis,
raising severe privacy concerns. To address this issue, researchers have
studied Federated Learning (FL)-based IDSs that train models across IoT devices
while keeping their data localized. However, the heterogeneity of data,
stemming from distinct vulnerabilities of devices and complexity of attack
vectors, poses a significant challenge to the effectiveness of FL models. While
current research focuses on adapting various ML models within the FL framework,
they fail to effectively address the issue of attack class imbalance among
devices, which significantly degrades the classification accuracy of minority
attacks. To overcome this challenge, we introduce FedMADE, a novel dynamic
aggregation method, which clusters devices by their traffic patterns and
aggregates local models based on their contributions towards overall
performance. We evaluate FedMADE against other FL algorithms designed for
non-IID data and observe up to 71.07% improvement in minority attack
classification accuracy. We further show that FedMADE is robust to poisoning
attacks and incurs only a 4.7% (5.03 seconds) latency overhead in each
communication round compared to FedAvg, without increasing the computational
load of IoT devices.
Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach
Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., Hossain, M.S.
Published: 2021
2022 IEEE 19th International Symposium on Biomedical Imaging
Fedsld: Federated learning with shared label distribution for medical image classification
Luo, J., Wu, S.
Published: 2022
Expert Systems with Applications
A review of recent approaches on wrapper feature selection for intrusion detection
Maldonado, J., Riff, M.C., Neveu, B.
Published: 2022
Applied Soft Computing
A survey and taxonomy of the fuzzy signature-based intrusion detection systems
M. Masdari, H. Khezri
Published: 2020
arxiv
被引用数 1
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
Published: 2016.2.18
Modern mobile devices have access to a wealth of data suitable for learning
models, which in turn can greatly improve the user experience on the device.
For example, language models can improve speech recognition and text entry, and
image models can automatically select good photos. However, this rich data is
often privacy sensitive, large in quantity, or both, which may preclude logging
to the data center and training there using conventional approaches. We
advocate an alternative that leaves the training data distributed on the mobile
devices, and learns a shared model by aggregating locally-computed updates. We
term this decentralized approach Federated Learning.
We present a practical method for the federated learning of deep networks
based on iterative model averaging, and conduct an extensive empirical
evaluation, considering five different model architectures and four datasets.
These experiments demonstrate the approach is robust to the unbalanced and
non-IID data distributions that are a defining characteristic of this setting.
Communication costs are the principal constraint, and we show a reduction in
required communication rounds by 10-100x as compared to synchronized stochastic
gradient descent.