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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: August 2022

Volume 7 | Issue 8

Impact factor: 8.15

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Paper Title: Hybrid Deep Learning for Botnet Attack Detection
Authors Name: Muniyasamy V , Mr.Ganesh kumar S , Manicka Mathavan M , Vijayaprabakar V
Unique Id: IJSDR2206044
Published In: Volume 7 Issue 6, June-2022
Abstract: Deep Learning is an efficient method for the botnet attack detection. Usually the volume of network traffic data and its required large memory space. Principal Component Analysis is one of the common linear methods like kernel methods, DL and Spectral methods employ non-linear transformation techniques. It is impossible to implement the DL method in memory constrained IOT devices. We reduce the large-scale IOT network traffic data using to reduction techniques. Bot dataset is the most common dataset that is publicly available for the network-based botnet attack detection. These traffic attack samples can be categorized into four botnet scenarios namely: DOS, DDOS, Information theft and Reconnaissance. Auto Encoder is an unsupervised method that produces lack of space representation of input data at the hidden layer. Different auto encoder architectures have been used to reduce the feature dimensionally in most popular intrusion datasets. We have to avoid the long short-term memory and to implement the convolutional Neural Network. Finally, the results show that the performance like more accuracy.
Keywords: Botnet Iot Attack, LSTM, CNN, Auto encoder
Cite Article: "Hybrid Deep Learning for Botnet Attack Detection ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 6, page no.273 - 278, June-2022, Available :http://www.ijsdr.org/papers/IJSDR2206044.pdf
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Publication Details: Published Paper ID: IJSDR2206044
Registration ID:200558
Published In: Volume 7 Issue 6, June-2022
DOI (Digital Object Identifier):
Page No: 273 - 278
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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