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7 March 2022 Machine learning methods in predicting portmapper DDoS attack
Tianyu Cai
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Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121672E (2022) https://doi.org/10.1117/12.2628593
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China
Abstract
With the continuous development of Internet technology, how to maintain network security has become an important issue. In recent year, the number of distributed denial-of-service (DDoS) attack has been rapidly grown. Hackers take advantage of the vulnerability in the network protocol to carry out DDoS attack to paralyze the server, causing massive economic losses. Therefore, it is significant for enterprise to accurately predict and defence against malicious DDoS attacks. This paper focuses on studying a type of reflection-based attack DDoS attack portmapper, which use a third-party remote procedure call (RPC) service to launch the attack. For the experiment, the principal components analysis (PCA) and recursive feature elimination (RFE) method are used to select the most essential features from the traffic packet. Based on the selected feature, this paper uses logistic regression and support vector machine (SVM) to do the prediction. The accuracy of both methods almost reach 95% as the input training dataset increases, which proves that these two method can perform well in predicting portmapper DDoS attack.
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Tianyu Cai "Machine learning methods in predicting portmapper DDoS attack", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121672E (7 March 2022); https://doi.org/10.1117/12.2628593
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KEYWORDS
Machine learning
Principal component analysis
Lawrencium
Data modeling
Internet
Feature selection
Network security
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