A-CAVE: Network abnormal traffic detection algorithm based on variational autoencoder

With the rapid development of the Internet and the rapid popularity of the network. Increasing network traffic also leads to frequent abnormal attacks, which seriously affects user information security. Then intrusion detection is studied as an active defense technology. In order to increase the abi...

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Bibliographic Details
Published in:ICT express Vol. 9; no. 5; pp. 896 - 902
Main Authors: Dong, Shi, Su, Huadong, Liu, Yue
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2023
Elsevier
한국통신학회
Subjects:
Anomaly traffic
Conditional variational autoencoder
Intrusion detection
Neural network
전자/정보통신공학
Conditional variational autoencoder
Anomaly traffic
Neural network
Intrusion detection
ISSN:2405-9595, 2405-9595
Online Access:Get full text
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Summary:With the rapid development of the Internet and the rapid popularity of the network. Increasing network traffic also leads to frequent abnormal attacks, which seriously affects user information security. Then intrusion detection is studied as an active defense technology. In order to increase the ability of network abnormal traffic detection, a network abnormal traffic detection algorithm based on variational autoencoder is proposed in this paper. In addition to normal data, the algorithm subdivides the intrusion attack types into 4 major categories, which contain 38 specific attacks, each specific attack category as an intrusion label, which will be used as the input of the decoder together with the hidden vector to obtain the classification results more accurately and efficiently and to recover the data features in the incomplete data set. Finally, the trained Deep Belief Nets (DBN) is used for further feature extraction and classification of the data. It is verified that the proposed variational autoencoder-based supervised classification scheme is simple and effective, which is able to identify new attacks well and outperforms other classification schemes in terms of data classification.
Bibliography:https://www.sciencedirect.com/science/article/pii/S2405959522001643
ISSN:2405-9595
2405-9595
DOI:10.1016/j.icte.2022.11.006