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E- conference s e r I e s premium best q uality guaran tee serviceBog'liq Italy Econference Series Nov 2023 package
Proceedings of International Educators Conference
Hosted online from Rome, Italy.
Date: 25
th
Nov., 2023
ISSN: 2835-396X Website: econferenceseries.com
84
the protocol identifier in the packet header. This method is simple and
fast but susceptible to deception and attacks.
2.
Port-based classification method: This method classifies traffic based
on port numbers, identifying the application type by determining the
source and destination port numbers of the packet. This method is also
simple and fast but vulnerable to port deception attacks.
3.
Feature extraction-based classification method: This method extracts
various features of packets, such as packet size and timestamp, to
classify traffic. This method requires manual selection and extraction of
features, which has some subjectivity.
4.
Deep learning-based classification method: This method uses deep
learning algorithms such as CNN and recurrent neural networks (RNN)
to classify and identify traffic. This method can automatically extract
features and has higher accuracy and flexibility.
5.
Deep learning-based classification method: This method uses deep
learning algorithms such as CNN and recurrent neural networks (RNN)
to classify and identify traffic. This method can automatically extract
features and has higher accuracy and flexibility.
With the widespread use of encryption and application software, network
traffic classification faces new challenges. In response to these challenges,
researchers have conducted a series of related work.
1.
Encryption traffic classification: Traditional network traffic
classification methods can not accurately identify the type of encrypted
traffic due to the difficulty of decrypting and analyzing encrypted traffic.
Researchers have proposed encryption traffic classification methods based
on traffic statistical features and machine learning algorithms, such as
Hidden Markov models (HMM) and collaborative decomposition algorithms.
2.
Application software traffic classification: The classification of
application software traffic is subjective and complex. Researchers have
proposed a number of methods to overcome these problems, such as
host behavior, user behavior, and deep learning.
3.
End-to-end representation learning: End-to-end representation learning
is a new traffic classification method that learns the end-to-end
representation of network traffic to achieve traffic classification and
identification. This method can overcome the problem of manual feature
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