dc.contributor.author |
Gohel, Amit M. |
|
dc.contributor.author |
Vanjara, Pratik A. |
|
dc.date.accessioned |
2023-05-16T05:31:41Z |
|
dc.date.available |
2023-05-16T05:31:41Z |
|
dc.date.issued |
2022-01 |
|
dc.identifier.citation |
Gohel,A.&Vanjara,P.(2022).A survey: Cyber security facet for machine learning algorithms.International Multidisciplinary journal of applied research,1(6),11-19 |
en_US |
dc.identifier.issn |
2321-7073 |
|
dc.identifier.uri |
http://10.9.150.37:8080/dspace//handle/atmiyauni/965 |
|
dc.description.abstract |
It is undeniably true that right now data is a really huge presence for all organizations or
associations. In this way ensuring its security is vital and the security models driven by
genuine datasets has become very significant. The activities dependent on military,
government, business and regular citizens are connected to the security and accessibility of
PC frameworks and organization. Starting here of safety, the organization security is a critical
issue on the grounds that the limit of assaults is constantly ascending throughout the long
term and they transform into be more modern and circulated. The target of this audit is to
clarify and look at the most usually utilized datasets. This paper centers cyber security aspect
to the various machine learning approaches such as Random Forest, SVM and KDD. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
International Multidisciplinary journal of applied research |
en_US |
dc.subject |
MACHINE LEARNING |
en_US |
dc.subject |
INTERNET TRAFFIC |
en_US |
dc.subject |
BIG DATA |
en_US |
dc.subject |
SECURITY |
en_US |
dc.subject |
CYBER SECURITY |
en_US |
dc.subject |
DETECTION SYSTEM |
en_US |
dc.title |
A survey: Cyber security facet for machine learning algorithms |
en_US |
dc.type |
Article |
en_US |