Research on abnormal network traffic detection based on 1D-CNN

Danyang Li; Ding Sun; Chuan Zeng

doi:10.1117/12.2615167

15 October 2021 Research on abnormal network traffic detection based on 1D-CNN

Danyang Li, Ding Sun, Chuan Zeng

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330N (2021) https://doi.org/10.1117/12.2615167
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

In view of the problem of complex feature engineering and poor learning ability in network traffic anomaly detection, this paper proposes an improved one-dimensional convolutional neural network(1D-CNN), which is mainly composed of a set of parallel convolutional layers, a global average pooling layer and a dropout layer. First, a parallel structure and an increasing number of convolution kernels are utilized to capture features of different scales. After feature fusion, global average pooling is used for information aggregation. Finally, the fully connected layer is applied for classification. Experiments on the model obtained by means of parameter tuning and training show that the improved 1D-CNN enhances the ability to learn features and reduces the information loss of the pooling zone between convolutional layers. The average precision reaches 95% and overfitting is suppressed. Compared with other machine learning algorithms, the proposed model has better performance in abnormal network traffic detection.

Citation Download Citation

Danyang Li, Ding Sun, and Chuan Zeng "Research on abnormal network traffic detection based on 1D-CNN", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330N (15 October 2021); https://doi.org/10.1117/12.2615167

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Tumor growth modeling

Convolution

Network security

Performance modeling

Machine learning

Neural networks

Convolutional neural networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years