|
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 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| 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 |
| Downloads: | 000337070 |
| 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 |
|
Click Here to Download This Article |
|
| 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
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn