Enhanced autoencoder and deep neural network (AE&DNN) method for ddos attack recognition

For the purpose of identifying attacks on the network systems, a monitoring method is essential. Identification of DDOS attacks is one of the most important concerns now a days in wireless networks. This paper proposed a security mechanism to identify DDoS attacks using enhanced AutoEncoder and Deep...

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Veröffentlicht in:Wireless networks Jg. 31; H. 4; S. 3277 - 3295
Hauptverfasser: Pajila, P. J. Beslin, Robinson, Y. Harold, Allimuthu, Udayakumar, Julie, E. Golden, Jaffrin, Lijetha. C., Sheena, B. Gracelin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2025
Springer Nature B.V
Schlagworte:
Algorithms
Artificial neural networks
Classification
Communications Engineering
Computer Communication Networks
Computer science
Datasets
Deep learning
Denial of service attacks
Electrical Engineering
Engineering
Internet service providers
Intrusion detection systems
IT in Business
Machine learning
Methods
Network security
Networks
Neural networks
Original Paper
Sensors
Support vector machines
Wireless networks
Datasets
Wireless sensor networks
DeepLearning
AutoEncoder
WSN-DS Datasets
Classification
ISSN:1022-0038, 1572-8196
Online-Zugang:Volltext
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Zusammenfassung:For the purpose of identifying attacks on the network systems, a monitoring method is essential. Identification of DDOS attacks is one of the most important concerns now a days in wireless networks. This paper proposed a security mechanism to identify DDoS attacks using enhanced AutoEncoder and Deep Neural Network (AE & DNN). In traditional methods, it is very difficult to classify the normal traffic in the network from the malicious one. Therefore some attacks will exist for a long time. So that the network will get affected by various attacks. To overcome these problems AutoEncoder is combined with Deep Neural Network, where AutoEncoder is mainly utilized for feature extraction and Deep Neural Network is used for classification. For validation, experiments have been done on the WSN-DS, CICIDS2017, and NSL-KDD standard datasets. From the experiments made, the proposed method outperformed than the CICIDS2017, and NSL-KDD datasets.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-025-03933-3