Convolutional neural network anomaly traffic detection based on DAPA

Zongrong Li; Denghui Ma; Ning Zhang; Nanfang Li; Xiang Li; Haishan Cao

doi:10.1117/12.2653205

20 October 2022 Convolutional neural network anomaly traffic detection based on DAPA

Zongrong Li, Denghui Ma, Ning Zhang, Nanfang Li, Xiang Li, Haishan Cao

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123501B (2022) https://doi.org/10.1117/12.2653205
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

At present, the existing anomaly traffic detection methods rely on the statistical characteristics of traffic for detection, which can not adapt to the unknown nature of network traffic, resulting in low detection accuracy, high false detection rate, and poor generalization ability of the methods. This paper studies the anomaly traffic detection method of a convolutional neural network based on the Dynamic Adaptive Pooling Algorithm (DAPA). The DAPA algorithm is used to improve the pooling layer of the CNN network to reduce the overfitting interference of unknown features; after the t-SNE algorithm reduces the dimensionality of the data, using clustering to transform the data feature map to get anomaly identification output. The experimental results show that the false detection rate is reduced by about 37.46%. The actual detection results are close to the prediction results, and the method has better generalization ability.

Citation Download Citation

Zongrong Li, Denghui Ma, Ning Zhang, Nanfang Li, Xiang Li, and Haishan Cao "Convolutional neural network anomaly traffic detection based on DAPA", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123501B (20 October 2022); https://doi.org/10.1117/12.2653205

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