Detection and prevention of Phishing Attacks

  • Abu Saad Choudhary
  • Rucha Desai
  • Lavkush Gupta
  • Madhuri Gedam

Abstract

Phishing is one amongst the main issues visaged by cyber-world and ends up in monetary losses for each industries and people. Detection of phishing attack with high accuracy has forever been a difficult issue. At present, visual similarities-based techniques square measure terribly helpful for police work phishing websites expeditiously. Phishing web site appearance terribly similar in look to its corresponding legitimate web site to deceive users into basic cognitive process that they are browsing the right web site. Visual similarity primarily based phishing detection techniques utilize the feature set like text content, text format, HTML tags, Cascading sheet (CSS), image, then forth, to form the choice. These approaches compare the suspicious web site with the corresponding legitimate web site by victimisation numerous options and if the similarity is larger than the predefined threshold price then it is declared phishing [2].

Keywords: Phishing Attack; URL; Real Time Model; Phishing Detection

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References

[1] Choon Lin Tan, Kang Leng Chiew, San Nah Sze , “Phishing Webpage Detection Using Weighted URL Tokens for Identity Keywords Retrieval”, in the proceedings of 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, pp 133-139, Springer Singapore, 2017.
[2] U Gürtürk, M Baykara, M Karabatak, “Identifying the Visitors with Data Mining Methods from Web Log Files”, International Journal of Emerging Technologies in Engineering Research (IJETER), 5(3), 243- 249, 2017.
[3] B. Gupta, A. Tewari, A. K. Jain, and D. P. Agrawal, “Fighting against phishing attacks: state of the art and future challenges,” Neural Computing and Applications, vol. 28, no. 12, pp. 3629–3654, 2017.
[4] A. Aleroud and L. Zhou, “Phishing environments, techniques, and countermeasures: A survey,” Computers & Security, vol. 68, pp. 160 – 196, 2017. [Online]. Available: http://
www.sciencedirect.com/science/article/pii/S01 67404817300810.
[5] Dipesh Vaya, Sarika Khandelwal, Teena Habpawat, “A Review on Visual Cryptography”, International Journal of Computer Applications, Volume.174 (Issue 05), ISSN: 0975- 8887, September 2017.
[6] The biggest phishing attacks of 2018 and what companies can dot prevent them in 2019, available at:
https://www.techrepublic.com/article/the- biggest-phishingattacks-of-2018-and-what- companies-can-do-to-prevent-themin-2
[7] P. Yi, Y. Guan, F. Zou, Y. Yao, W. Wang and T. Zhu, "Web Phishing Detection Using a Deep Learning Framework", Wireless Communications and Mobile Computing, vol. 2018, pp. 1-9, 2018
[8] K. L. Chiew, J. S.-F. Choo, S. N. Sze and K.
S. C. Yong, "Leverage Website Favicon to Detect Phishing Websites", Security and Communication Networks, vol. 2018, pp. 1- 11, 2018.
[9] A. Tewari, A. K. Jain, and B. B. Gupta, “Recent survey of various defense mechanisms against phishing attacks,” Journal of Information Privacy and Security, vol. 12, no. 1, pp. 3–13, 2016.
[10] A. K. Jain and B. B. Gupta, “A novel approach to protect against phishing attacks at client side using auto-updated white- list,” EURASIP Journal on Information Security, vol. 2016, article 9, 11 pages, 2016
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How to Cite
Choudhary, A. S., Desai, R., Gupta, L., & Gedam, M. (2021). Detection and prevention of Phishing Attacks. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 7(1), 193-196. https://doi.org/10.33130/AJCT.2021v07i01.038