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International Journal of Scientific Development and Research - IJSDR
(An International Open Access Journal)
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2455-2631


Issue: March 2021

Volume 6 | Issue 3

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Paper Title: KIDS: For Key Recovery Attacks and Security of Machine Learning
Authors Name: Pooja Mande , Prof. K. S. Kore
Unique Id: IJSDR1807032
Published In: Volume 3 Issue 7, July-2018
Abstract: In recent era the use of internet become amplified extremely. Most of people used internet to convey their data and used cloud to save it. There is chance that the data may get scythed and get tainted. Since most current network attacks happen at the application layer, analysis of packet payload is necessary for their detection. To improved security from such unauthorized users various Anomaly intrusion detection schemes are introduced recently. To defeat these troubles one such structure is Keyed Intrusion Detection System is application layer network anomaly detection system which is based on principle which is much same as the working of some cryptographic primitives. By adding mystery component in to plan so that a couple of operations are becomes impractical without knowing the key. Core idea to make evasion attacks more difficult is to add the concept of a “key” which is the secret element used to determine how classification features are extracted from the payload. Key is different for each implementation of the method and is kept secret. Therefore model of normal payload is secret although detection method is public. In KIDS the scholarly model and the irregularity's calculation score are both key-subordinate, a reality which obviously keeps an aggressor from making shirking assaults. In this recovering the key is to marvelously straightforward and require that attacker can collaborate with KIDS and get criticism about examining solicitations. Here present realistic attacks for two different adversarial settings and show that recovering the key requires only a small amount of queries.
Keywords: Anomaly Detection, Intrusion Detection Systems, Machine Learning.
Cite Article: "KIDS: For Key Recovery Attacks and Security of Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.3, Issue 7, page no.182 - 188, July-2018, Available :http://www.ijsdr.org/papers/IJSDR1807032.pdf
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Publication Details: Published Paper ID: IJSDR1807032
Registration ID:180523
Published In: Volume 3 Issue 7, July-2018
DOI (Digital Object Identifier):
Page No: 182 - 188
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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