FEATURE EXTRACTION FOR MALICIOUS URL DETECTION IN DATA MINING

Authors

  • Mr. Jadhav Bharat S. Dr. Gumaste S.V. Author

Keywords:

TTL, Cost Sensitivity. Cost sensitive Classification, online anomaly detection, online learning

Abstract

In this paper we have discussed the section of feature extraction with cost sensitivity. In This Section of Feature Extraction We have proposed different features for detecting Whether the URL is malicious or not . The different features are:- Length of the URL, Number of full-stops in the URL, TTL of the URL, and Get Info which gives information about the Registrar of the URL. Date of the URL, the malicious URL discovery framework uses genuine dataset .and with the help of the above feature extraction we will be able to detect whether the URL is Malicious or not

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Published

2015-07-14

Issue

Section

Articles

How to Cite

FEATURE EXTRACTION FOR MALICIOUS URL DETECTION IN DATA MINING. (2015). Global Journal of Advanced Engineering Technologies and Sciences, 2(7), 5-8. https://gjaets.com/index.php/gjaets/article/view/262

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