Open Access   Article Go Back

Phishing URL Detection using Neural Network Optimized by Cultural Algorithm

A. Haider1 , R. Singh2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 860-863, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.860863

Online published on Jul 31, 2018

Copyright © A. Haider, R. Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: A. Haider, R. Singh, “Phishing URL Detection using Neural Network Optimized by Cultural Algorithm,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.860-863, 2018.

MLA Style Citation: A. Haider, R. Singh "Phishing URL Detection using Neural Network Optimized by Cultural Algorithm." International Journal of Computer Sciences and Engineering 6.7 (2018): 860-863.

APA Style Citation: A. Haider, R. Singh, (2018). Phishing URL Detection using Neural Network Optimized by Cultural Algorithm. International Journal of Computer Sciences and Engineering, 6(7), 860-863.

BibTex Style Citation:
@article{Haider_2018,
author = {A. Haider, R. Singh},
title = {Phishing URL Detection using Neural Network Optimized by Cultural Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {860-863},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2525},
doi = {https://doi.org/10.26438/ijcse/v6i7.860863}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.860863}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2525
TI - Phishing URL Detection using Neural Network Optimized by Cultural Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - A. Haider, R. Singh
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 860-863
IS - 7
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
339 311 downloads 148 downloads
  
  
           

Abstract

Internet scams are numerous and varied. Anyone is likely to be the target of an attack while browsing the net. More and more crooks do not hesitate to use Social Engineering as a lever to acquire sensitive data unfairly by exploiting human flaws. Phishing is a Social Engineering technique used by these hackers. It is used to steal personal information in order to commit an identity theft without the knowledge of their victims. The persuasion power of these crooks is the keystone of a successful attack. This work aims to collect, map and model elements that will lead to the finding of phishing URL automatically, for this purpose data mining is used as basic tools, in this sense, it is considered that the existing patterns in a URL make it possible to distinguish the legitimate link for pages, the identification of these patterns will serve to model a successful classification method, for this purpose, the attributes found in the database "phishing web" that correspond to patterns of phishing pages will be validated, at the same time will be evaluated algorithms extracted from the literature that allow a better classification of records, finally, a model with the highest precision results is delivered which consists of cultural algorithm optimized neural network classifier.

Key-Words / Index Term

Cultural Algorithm, Neural Network, Phishing URL

References

[1] C. Whittaker, B. Ryner and M. Nazif, “Large-scale automatic classification of phishing pages,” in In: Proc. 17th Annual Network and Distributed System Security Symposium, NDSS‟10, San Diego, CA, USA, 2010.
[2] Basnet, Ram B., Andrew H. Sung and Quingzhong Liu, ““Learning to detect phishing URLs,” IJRET: International Journal of Research in Engineering and Technology, vol. 6, pp. 11-24, 2014.
[3] Xiang G., Hong J., Rose C. P. and Cranor L. , “CANTINA+: A feature-rich machine learning framework for detecting phishing Web sites,” ACM Trans. Inf. Syst. Secur. 14, 2, Article 21, p. 28, September 2011.
[4] Christos Stergiou and Dimitrios Siganos, “Neural Networks”, Report available at: http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
[5] Reynolds, Robert G. "An introduction to cultural algorithms." In Proceedings of the third annual conference on evolutionary programming, pp. 131-139. Singapore, 1994.