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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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Paper Title:	INTRUSION DETECTION SYSTEM FOR CLOUD BASED ON NAIVE BAYES AND HASHING
Authors Name:	Amruta Deshmukh , Kirti Girdhani , Nayan Keskar , Prajakta Gite , Prof. D. D. Sharma
Unique Id:	IJSDR2201007
Published In:	Volume 7 Issue 1, January-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:	Information Security, Intrusion Detection, Data Mining
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 1, page no.108 - 110, January-2022, Available :http://www.ijsdr.org/papers/IJSDR2201007.pdf
Downloads:	000337070
Publication Details:	Published Paper ID: IJSDR2201007 Registration ID:193854 Published In: Volume 7 Issue 1, January-2022 DOI (Digital Object Identifier): Page No: 108 - 110 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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