Website Reputation System
B. Amutha1, Prabhav Gupta2, Himanshu Kumar3
1B. Amutha, Department of Computer Science, SRM Institute of Science and Technology, Chennai, India.
2Prabhav Gupta, Department of Computer Science, SRM Institute of Science and Technology, Chennai, India.
3Himanshu Kumar, Department of Computer Science, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1228-1233 | Volume-8 Issue-11S September 2019 | Retrieval Number: K124809811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1248.09811S19
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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: Because of the fast development of the web, sites have turned into the interloper’s principle target. As the quantity of web pages expands, the vindictive pages are likewise expanding and the assault is progressively turned out to be modern developing different ways to trick a client into visiting malicious websites extracting credential information. This paper presents a detailed account of ensemble based machine learning approach for URL classification. Models already existing either use outdated techniques or limited set of features in their attack detection model and thus leads to lower detection rate. But ensemble classifiers along with a selection of robust feature list for single and multi attack type detection outperform all the previous deployed techniques. Focus of the study is being able to come up with a system model that yields us better results with a higher accuracy rate.
Keywords: Data Mining, Classifiers, Multiclass, Layered approach, Multi-Classificat
Scope of the Article: Data Mining