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2019 | OriginalPaper | Buchkapitel

DDoS Attack Detection Method Based on V-Support Vector Machine

verfasst von : Xiangyan Tang, Rui Cao, Jieren Cheng, Dong Fan, Wenxuan Tu

Erschienen in: Cyberspace Safety and Security

Verlag: Springer International Publishing

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Abstract

The characteristics of distributed denial of service (DDoS) attack diversity, distribution and burstiness in the new network environment make it difficult to detect the current detection methods. This paper proposes a DDoS attack detection method based on V-Support Vector Machine (SVM). This method defines a nine-tuple network service association feature to extract the feature of the network flow, then normalizes the feature data and reduces the dimension by principal component analysis. Finally, select the appropriate kernel function and introduce the parameter V control support vector and the number of error vectors, establish a V-SVM-based DDoS attack classification model to detect attacks. The experimental results show that compared with similar methods, this method not only improves the accuracy, reduces the false negative rate, but also ensures the stability and timeliness of the classification model.

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Vorheriges Kapitel Multi-view DDoS Network Flow Feature Extraction Method via Convolutional Neural Network

Nächstes Kapitel DDOS Multivariate Information Fusion Model Based on Hierarchical Representation Learning

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Titel: DDoS Attack Detection Method Based on V-Support Vector Machine
verfasst von: Xiangyan Tang
Rui Cao
Jieren Cheng
Dong Fan
Wenxuan Tu
Verlag: Springer International Publishing
Buch: Cyberspace Safety and Security
Print ISBN: 978-3-030-37351-1

Electronic ISBN: 978-3-030-37352-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-37352-8_4

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