A comprehence study of DDoS attack detecting algorithm using GRU-BWFA classifier

The suggested classifier, Gated Recurrent Unit Neural Network, is utilised to produce the appropriate classification in this research. It is based on Bidirectional weighted feature averaging (GRU-BWFA). The findings show that each classifier performs differently, with the accuracy rate ranging betwe...

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Published in:Measurement. Sensors 2022-12, Vol.24, p.100570, Article 100570
Main Authors: Gangula, Rekha, Mohan, V. Murali, Kumar, Ranjeeth
Format: Article
Language:English
Subjects:
Attack
Classification
DDoS
Deep learning
GRU-BWFA
Recurrent neural network
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container_title Measurement. Sensors
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description The suggested classifier, Gated Recurrent Unit Neural Network, is utilised to produce the appropriate classification in this research. It is based on Bidirectional weighted feature averaging (GRU-BWFA). The findings show that each classifier performs differently, with the accuracy rate ranging between the three. GRU-BWFA, the suggested system, has the greatest accuracy rate of 99.9%. The suggested approach outperformed current algorithms to categorise and identify several attacks from the SNMP-MIB dataset, including TCP-SYN, UDP flood, ICMP-echo, HTTP flood, Slow Loris, Slow Post, and Brute force, with a detection rate of 99.9%. As a result, the System is effectively restored in the shortest possible time.
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subjects Attack
Classification
DDoS
Deep learning
GRU-BWFA
Recurrent neural network
title A comprehence study of DDoS attack detecting algorithm using GRU-BWFA classifier
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