Detection of Malicious Web Pages Using Machine Learning Technique

<span title="2020-10-15">2020</span> <i title="The World Academy of Research in Science and Engineering"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/naqzxq5hurh2bp2pnvwitnnx44" style="color: black;">International Journal of Advanced Trends in Computer Science and Engineering</a> </i> &nbsp;
The Internet is used as a convenient channel for the distribution of information and resource sharing. This platform is made efficient and accessible by the use of search engines (electronic library) to effectively satisfy the needs of various users. Most URL are dishonest and sometimes position itself at the top of the engines. This work detects such malicious URLs using a built machine learning which we implemented to organize Uniform Resource Locator (URL) into two categories -trustworthy
more &raquo; ... untrusted. This Classifier will calculate a trust score for each user in a particular URL as to ascertain the trustworthiness, which will invariably determine the safety of web pages. This trust score will be used to decide a trusted webpage, should give accuracy of not less than 98.9% and a reasonable feature measure. The dataset are upload to the application to build model deployed from a general framework for malicious URL detection in order to predict, classify a URL and was implemented using naïve Bayes. And the URL is entered and click on Detect & Analyze and the result is displayed. This trust score of the feature extract will be used to confirm if an Internet link is trusted or not.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.30534/ijatcse/2020/243952020">doi:10.30534/ijatcse/2020/243952020</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qpgmch6qwnewppr27j7kvjuuey">fatcat:qpgmch6qwnewppr27j7kvjuuey</a> </span>
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