The transfer learning methodology initiates research into techniques that will allow AI model development and deployment without relying on large, specific data ...
Transfer learning – which allows us to take information learned in one domain and apply it to another – provides one way to create and distribute these models ...
Details ; Title. Transfer learning for raw network traffic detection ; Is Part Of. Expert systems with applications, 2023-01, Vol.211, p.118641, Article 118641.
In this paper, we propose a ConvNet model using transfer learning for the network intrusion detection. The model consists of two concatenated ConvNets and is ...
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Feb 21, 2019 · The results showed the transfer learning approaches improve the performance of detecting unknown network attacks compared to baselines.
You need to enable JavaScript to run this app. 10.1016/j.eswa.2022.118641. Transfer learning for raw network traffic detection. Dec 31, 2022. Associated with this document.
This study demonstrates that deep transfer learning techniques make it possible to construct large deep learning models to perform network classification, which ...
In this paper we address the feature selection problem for network traffic based anomaly detection. We propose a multi-stage feature selection method using ...
This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effective ...
A transfer learning-based method TL- ConvNet was introduced that learns from a base dataset and transfers the learned knowledge to the learning of the target ...