Implementing Machine Learning Algorithms to Indentify Distributed Denial-of-Service Attacks

An Internet server, service, or network may be the subject of a distributed denial-of-service (DDoS) assault, in which the attacker attempts to interrupt regular traffic by flooding the target with an excessive amount of data requests. Network security has been severely compromised as a result. We c...

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Bibliographic Details
Published inICIIP ... proceedings (Online) pp. 436 - 441
Main Authors Mehra, Ankush, Singh, Gurpreet, Badotra, Sumit, Kaur, Balwinder, Verma, Amit
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.11.2023
Subjects
DDoS
DDoS attack prediction
Feature extraction
Internet
Intrusion detection system
Machine Learning
Machine learning algorithms
Network security
PCA
Prediction algorithms
Reviews
RNN
Servers
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Summary:An Internet server, service, or network may be the subject of a distributed denial-of-service (DDoS) assault, in which the attacker attempts to interrupt regular traffic by flooding the target with an excessive amount of data requests. Network security has been severely compromised as a result. We choose the vast majority of network parameters to characterize the traffic in order to get a complete picture of it. As a further step toward lowering the detection time complexity, we employ the PCA approach to compress the features' dimensions. Using principal component analysis (PCA), we can save much of the original data while drastically cutting down on forecast time. After data dimensionality reduction, the RNN is used to train and get a detection model. In comparison to other approaches for detecting DDoS assaults, PCA-RNN performs much better on the real-world dataset, as shown by the evaluation results.
ISSN:2640-074X
DOI:10.1109/ICIIP61524.2023.10537743