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  Published Paper Details:

  Paper Title

MALICIOUS URL DETECTION USING 1D CNN AND TENSORFLOWJS

  Authors

  Harish S,  T Veni Priya M.tech.,(Ph.D),  Venkatramanan M,  Suraj B

  Keywords

1D CNN, URL, browser extension, machine learning, phishing websites, neural network,deep learning, tensorflowJS.

  Abstract


Phishing web sites have been a serious chance to cyber safety which employs each social engineering and technical trick to steal clients� non-public identity statistics and monetary account information. Recent years have proved the hasty increase of phishing attacks. Phish recognizer is targeted on getting rid of the identity robbery and fraud that result from the growing problem of phishing. We propose phish recognizer, a deep learning-based method that uses 1D CNN to build an accurate function representation of URLs, which we use to teach a phishing URL classifier. In conclusion, the model that has been trained has a high precision and recall value, and the model is converted to tensorflowJS model to run inside a browser extension.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2009235

  Paper ID - 198615

  Page Number(s) - 1847-1851

  Pubished in - Volume 8 | Issue 9 | September 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Harish S,  T Veni Priya M.tech.,(Ph.D),  Venkatramanan M,  Suraj B,   "MALICIOUS URL DETECTION USING 1D CNN AND TENSORFLOWJS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 9, pp.1847-1851, September 2020, Available at :http://www.ijcrt.org/papers/IJCRT2009235.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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