Federated learning for intrusion detection system: Concepts, challenges and future directions
The rapid development of the Internet and smart devices trigger surge in network traffic
making its infrastructure more complex and heterogeneous. The predominated usage of …
making its infrastructure more complex and heterogeneous. The predominated usage of …
[HTML][HTML] Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges
Abstract The application of Machine Learning (ML) techniques to the well-known intrusion
detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …
detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …
Federated deep learning for zero-day botnet attack detection in IoT-edge devices
Deep learning (DL) has been widely proposed for botnet attack detection in Internet of
Things (IoT) networks. However, the traditional centralized DL (CDL) method cannot be …
Things (IoT) networks. However, the traditional centralized DL (CDL) method cannot be …
A survey on in-network computing: Programmable data plane and technology specific applications
S Kianpisheh, T Taleb - IEEE Communications Surveys & …, 2022 - ieeexplore.ieee.org
In comparison with cloud computing, edge computing offers processing at locations closer to
end devices and reduces the user experienced latency. The new recent paradigm of in …
end devices and reduces the user experienced latency. The new recent paradigm of in …
A survey on data plane programming with p4: Fundamentals, advances, and applied research
Programmable data planes allow users to define their own data plane algorithms for network
devices including appropriate data plane application programming interfaces (APIs) which …
devices including appropriate data plane application programming interfaces (APIs) which …
Semisupervised federated-learning-based intrusion detection method for internet of things
Federated learning (FL) has become an increasingly popular solution for intrusion detection
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …
to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based …
In-network machine learning using programmable network devices: A survey
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …
classification and anomaly detection to network configuration. However, machine learning …
Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach
The constantly evolving digital transformation imposes new requirements on our society.
Aspects relating to reliance on the networking domain and the difficulty of achieving security …
Aspects relating to reliance on the networking domain and the difficulty of achieving security …
Automating in-network machine learning
Using programmable network devices to aid in-network machine learning has been the
focus of significant research. However, most of the research was of a limited scope …
focus of significant research. However, most of the research was of a limited scope …
IIsy: Practical in-network classification
C Zheng, Z Xiong, TT Bui, S Kaupmees… - arXiv preprint arXiv …, 2022 - arxiv.org
The rat race between user-generated data and data-processing systems is currently won by
data. The increased use of machine learning leads to further increase in processing …
data. The increased use of machine learning leads to further increase in processing …