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AnAomaly detection algorithm using deep Autoencoder in python

My project is about intrusion detection or classification in IoT network traffic. I have proposed something using "Stacked Conditional Variational Autoencoder" for solving data imbalance issue and an DNN for classification which I need to implement in python. And also need to compare results with benchmark algorithms. I have 3 intrusion detection datasets.

so I need to build model along with some base models provided in the article I will provide. the base model is like this: they used"Conditional Variational Autoencoder" for generating new data by training the original data. second they merged the newly generated data with original data. 3rd they used DNN algorithm o train that merged data to classify the class/labels.

Compétences : Python, Deep Learning, Sécurité Informatique, Science des données

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Concernant l'employeur :
( 0 commentaires ) Kuala Lumpur, Malaysia

Nº du projet : #27894508

16 freelances font une offre moyenne de 185 $ pour ce travail

schoudhary1553

Hi, Greetings! ✅checked your project details: AnAomaly detection algorithm using deep Autoencoder in python ✅Completed Time: In project deadline We have worked on 600 + Projects. I have 6 + years of the experie Plus

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6.3
mubashirabbas07

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5.9
viduWEBmaster

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(6 Commentaires)
3.8
pallaviagarwal13

Hi, Happy to chat with you about what you have in mind. I'm working on anomaly detection in Raspberry Pi and can help you with this.

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3.3
jarviskb

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3.4
MedaniAhmed

Hi there.. I have just seen your posted project and it seems very interesting. The deep autoencoder is employed as a generative pretraining network, and the dynamic neural network as a classifier. I have already done Plus

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(3 Commentaires)
3.1
sajjadtaghvaeifr

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2.3
Baha2020

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1.9
ledphong

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2.3
JaT83

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protaga

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Soham1024

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insidetech74

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technozen

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0.0