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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: June 2022

Volume 7 | Issue 6

Impact factor: 8.15

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Paper Title: INTRUSION DETECTION SYSTEM FOR CLOUD BASED ON NAIVE BAYES AND HASHING
Authors Name: Amruta Deshmukh , Kirti Girdhani , Nayan Keskar , Prajakta Gite
Unique Id: IJSDR2205014
Published In: Volume 7 Issue 5, May-2022
Abstract: Network traffic in the cloud computing environment is characterized by large scale, high dimensionality, and high redundancy, these characteristics pose serious challenges to the development of cloud intrusion detection systems. Deep learning technology has shown considerable potential for intrusion detection. Therefore, this study aims to use deep learning to extract essential feature representations automatically and realize high detection performance efficiently. An effective stacked contractive autoencoder (SCAE) method is presented for unsupervised feature extraction. By using the SCAE method, better and robust low-dimensional features can be automatically learned from raw network traffic. We are creating a system that allows user to provide security to their files and protect them from hacker and avoid the malicious attacks, we are encrypting the password by hash and also if the hacker break are hash code it will get only the dummy data, OTP will also will there for authentication.
Keywords: Cloud computing, Data security, encryption and decryption, data storage in cloud.
Cite Article: "INTRUSION DETECTION SYSTEM FOR CLOUD BASED ON NAIVE BAYES AND HASHING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 5, page no.74 - 79, May-2022, Available :http://www.ijsdr.org/papers/IJSDR2205014.pdf
Downloads: 00096794
Publication Details: Published Paper ID: IJSDR2205014
Registration ID:200348
Published In: Volume 7 Issue 5, May-2022
DOI (Digital Object Identifier):
Page No: 74 - 79
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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