Advanced Machine Learning Models to Handle Unifying Attacks in Images
K. P. Sai Rama Krishna1, K. Sravani2
1K.P.Sai Rama Krsihna, Department of Computer Science and Enginerring, S.R.K.R Engineering College, Bhimavaram, India.
2K. Sravani, Department of Computer Science and Engineering, S.R.K.R Engineering College, Bhimavaram, India.
Manuscript received on 12 August 2019 | Revised Manuscript received on 20 August 2019 | Manuscript published on 30 August 2019 | PP: 4336-4339 | Volume-8 Issue-10, August 2019 | Retrieval Number: J98300881019/2019©BEIESP | DOI: 10.35940/ijitee.J9830.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Machine Learning, Embedding Watermarking, Neural Networks, Digital Watermarking.
Scope of the Article: Machine Learning