Welcome to IJSDR UGC CARE norms ugc approved journal norms IJRTI Research Journal | ISSN : 2455-2631
IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.15 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Issue: August 2022

Volume 7 | Issue 8

Impact factor: 8.15

Click Here For more Info

Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter

Copyright Infringement Claims
Indexing Partner
Published Paper Details
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
Downloads: 000101773
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

Click Here to Download This Article

Article Preview

Click here for Article Preview







Major Indexing from www.ijsdr.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

Track Paper
Important Links
Conference Proposal
ISSN
DOI (A digital object identifier)


Providing A digital object identifier by DOI
How to GET DOI and Hard Copy Related
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Creative Commons License
This material is Open Knowledge
This material is Open Data
This material is Open Content
Social Media
IJSDR

Indexing Partner