KIDS: For Key Recovery Attacks and Security of Machine Learning
Pooja Mande
, Prof. K. S. Kore
Anomaly Detection, Intrusion Detection Systems, Machine Learning.
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.
"KIDS: For Key Recovery Attacks and Security of Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 7, page no.182 - 188, July-2018, Available :https://ijsdr.org/papers/IJSDR1807032.pdf
Volume 3
Issue 7,
July-2018
Pages : 182 - 188
Paper Reg. ID: IJSDR_180523
Published Paper Id: IJSDR1807032
Downloads: 000346998
Research Area: Engineering
Country: --, -, -
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
