Open Access   Article Go Back

RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web

V. Karamchand Gandhi1 , M. Suriakala2

  1. PG and Research Department of Computer Science, Dr Ambedkar Government Arts College (Autonomous), Chennai, India.
  2. PG and Research Department of Computer Science, Dr Ambedkar Government Arts College (Autonomous), Chennai, India.

Correspondence should be addressed to: vedhagandhi@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 74-78, Sep-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i9.7478

Online published on Sep 30, 2017

Copyright © V. Karamchand Gandhi, M. Suriakala . 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: V. Karamchand Gandhi, M. Suriakala, “RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.74-78, 2017.

MLA Style Citation: V. Karamchand Gandhi, M. Suriakala "RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web." International Journal of Computer Sciences and Engineering 5.9 (2017): 74-78.

APA Style Citation: V. Karamchand Gandhi, M. Suriakala, (2017). RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web. International Journal of Computer Sciences and Engineering, 5(9), 74-78.

BibTex Style Citation:
@article{Gandhi_2017,
author = {V. Karamchand Gandhi, M. Suriakala},
title = {RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2017},
volume = {5},
Issue = {9},
month = {9},
year = {2017},
issn = {2347-2693},
pages = {74-78},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1433},
doi = {https://doi.org/10.26438/ijcse/v5i9.7478}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i9.7478}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1433
TI - RSIPS: A Robust System to Identify Phishing Websites using Unique Addressing features of Web
T2 - International Journal of Computer Sciences and Engineering
AU - V. Karamchand Gandhi, M. Suriakala
PY - 2017
DA - 2017/09/30
PB - IJCSE, Indore, INDIA
SP - 74-78
IS - 9
VL - 5
SN - 2347-2693
ER -

VIEWS PDF XML
843 501 downloads 293 downloads
  
  
           

Abstract

Phishing is a form of internet fraud in which an attacker, also known as a phisher, attempts to fraudulently retrieve legitimate users` confidential or sensitive credentials by imitating electronic communications from a trustworthy or from the public organization in an automated fashion. There is an need of identify the phishing websites in this emerging digital era. Based on the URL and content based features of websites like length of URL, domain’s age, WHOIS properties, etc, we can draw an algorithm to identify the phishing websites. Furthermore, our approach checks the legitimacy of a webpage using hyperlink features. Hyperlinks are extracted from the source code of the given website and apply that into the proposed algorithm to detect phishing site. Our experiment shows that our proposed algorithm is very effective to detect the phishing websites and it have 89.16% True Positive Rate while greater than 82% of accuracy.

Key-Words / Index Term

Phishing URL, Phishing URL/Hyperlink

References

[1] S. Abu-Nimeh and S. Nair, “Bypassing security toolbars and phishing filters via dns poisoning” In IEEE GLOBECOM: Global Telecommunications Conference, 2008.
[2] S. Sheng, M. Holbrook, P. Kumaraguru, L. F. Cranor, and J. Downs, “Who falls for phish? : a demographic analysis of phishing susceptibility and effectiveness of interventions” in Proceedings of the 28th international conference on Human factors in computing systems, ser. CHI ’10. New York, NY, USA: ACM, 2010, pp. 373–382.
[3]B. Krebs, “HBGary Federal hacked by Anonymous” http://krebsonsecurity.com/2011/02/hbgary-federal-hacked-by-anonymous/, 2011, accessed December 2011.
[4] V Karamchand Gandhi, “An Overview Study on Cyber crimes in Internet” Journal of Information Engineering and Applications from IISTE, Volume 2, Number 1, pages 1-5, February 2012. ISSN 2224-5782 (Paper) ISSN 2225-0506 (Online).
[5] Google safe browsing API Available at: https://developers.google.com/safe-browsing/. Accessed 30 Nov 2015.
[6] W Liu, X Deng, G Huang, AY Fu, “An antiphishing strategy based on visual similarity assessment”, IEEE Internet Comput. 10(2), 58–65 (2006)
[7] VP Reddy, V Radha, M Jindal, “Client side protection from phishing attack”, International Journal of advanced Engineering Science Technology 3(1), 039–045 (2011)
[8] A. Z. Broder, “Identifying and filtering near-duplicate documents”, In COM ’00: Proceedings of the 11th Annual Symposium on Combinatorial Pattern Matching, pages 1–10, London, UK, 2000. Springer-Verlag.