UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 4
April-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2004150


Registration ID:
230655

Page Number

1090-1104

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Title

CLASSIFICATION OF SKEPTICAL URLS - AN APPROACH USING MACHINE LEARNING

Abstract

Our computers are always susceptible to the attackers who are on a constant outlook for one trivial lapse that cybernauts might commit. From this standpoint, the study in question, mainly deals with the machine learned classification algorithms that are employed to detect the skeptical URLs and categorizing them into respective types namely malware, benign, spam, phishing, and defacement URLs. The approach is initiated by importing the data set and priming it with required pre-processing techniques to prepare it for the algorithm exposure. The assignment can be carried out using the numerous python libraries on a platform, Anaconda. This platform contains Jupyter notebook, a web application which is used to create and share the documents with live code , numerical and narrative data cleaning, etc. It is an open source web application. The algorithms used were classification algorithms namely Random forest classifier (RFC),K -nearest neighbors (KNN) and Decision tree algorithm. After pre-processing the respective data set, it undergoes the training and testing through which the accuracy of the algorithm is determined.

Key Words

Machine learning, Cyber security , URLs , Classification algorithms, KNN, Data set, RFC , Decision tree , Python , Jupytor

Cite This Article

"CLASSIFICATION OF SKEPTICAL URLS - AN APPROACH USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1090-1104, April-2020, Available :http://www.jetir.org/papers/JETIR2004150.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"CLASSIFICATION OF SKEPTICAL URLS - AN APPROACH USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1090-1104, April-2020, Available at : http://www.jetir.org/papers/JETIR2004150.pdf

Publication Details

Published Paper ID: JETIR2004150
Registration ID: 230655
Published In: Volume 7 | Issue 4 | Year April-2020
DOI (Digital Object Identifier):
Page No: 1090-1104
Country: Hyderabad / Medchal, Telangana, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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