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ATTENTION MECHANISM AND SPATIOTEMPORAL FEATURESBog'liq Italy Econference Series Nov 2023 packageATTENTION MECHANISM AND SPATIOTEMPORAL FEATURES
Senior Lecturer: Korotkova Larisa Aleksandrovna
Tashkent State Technical University.
Department: radio devices and systems.
Yuldasheva Diyora Ravshanovna
2nd year student:
Abstract
Traffic classification is widely used in network security and network m
anagement. Early studies havemainly focused on mapping network
traffic to different unencrypted applications, but little research has been
done on network traffic classification of encrypted applications, especially
the underlying traffic of encrypted applications. To address the above
issues, this paper proposes a network encryption traffic classification
model that combines attention mechanisms and spatiotemporal features.
The model firstly uses the long short-term memory (LSTM) method to
analyze continuous network flows and find the temporal correlation
features between these network flows. Secondly, the convolutional neural
network (CNN) method is used to extract the high-order spatial features
of the network flow, and then, the squeeze and excitation (SE) moded
is used to weight and redistribute the high-order spatial features to
obtain the key spatial features of the network flow. Finally, through the
above three stages of training and learning, fast classification of network
flows is achieved.
Related work
Traditional network traffic classification refers to the method of classifying
and identifying traffic by analyzing the packet features of network traffic.
There has been a lot of research in this field, mainly including the
following aspects:
1.
Protocol-based classification method: This is one of the earliest
network traffic classification methods, which classifies traffic by identifying
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