Advanced machine learning models to handle unifying attacks in images

0Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Critical advancement has been made with profound neural systems as of late. Sharing prepared models of profound neural systems has been a significant in the fast advancement of innovative work of these frameworks. In digital environment, there are different types of applications face security related attack sequences from third parties. Most of the machine learning related approaches was introduced to describe security in wind and vulnerable attack sequences. Digital Watermarking is one of the approach to handle adversary related security approach to handle attacks appeared in digital environment. But it has some limitations to describe efficient security behind the web related applications appeared in real time environment. So that in this paper, we propose and implement advanced machine learning approach i.e Neural Network based Click Prediction (NNBCP) to handle web related attack sequences in real time environment. It uses Integrated CAPTCHA procedure to provide machine learning based captcha generation for user login and registration to handle different types of attacks in digital systems.

Cite

CITATION STYLE

APA

Sai Rama Krishna, K. P., & Sravani, K. (2019). Advanced machine learning models to handle unifying attacks in images. International Journal of Innovative Technology and Exploring Engineering, 8(10), 4336–4339. https://doi.org/10.35940/ijitee.J9830.0881019

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free