Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 22 April,2024 Pages:1930-1935
Phishing websites are very dangerous as they exploit the vulnerabilities of people to get access to people's sensitive information and data. These type of websites later pretend to be legitimate sources to deceive users into revealing their personal data. Attackers usually target on the common details like email IDs and credit card numbers being common targets. Detecting phishing kind of attacks is critical because they are becoming more and more hostile. Phishers often choose websites that appear visually and semantically identical to genuine ones. Here the protective measure aims to safeguard users security, prevent access to fake users, hacked, or undesirable URLs, and to give the trust to the users of our website towards their data privacy and protection from the phishing Advertisement.
Review analysis, Product comparison, Phishing detection, Blocking page redirection
1. Ayman Al-Ani , Ahmed K. Al-Ani ,Shams A. Laghari , Selvakumar Manickam Khin Wee Lai , And Khairunnisa Hasikin, “NDPsec: Neighbor Discovery Protocol Security Mechanism,” Volume 10, pp. 83650 – 83663, 2022. 2. Lakshmana Rao Kalabarige, Routhu Srinivasa Rao, Ajith Abraham , And Lubna Abdelkareim Gabralla, “Multilayer Stacked Ensemble Learning Model to Detect Phishing Websites,” Volume 10, pp. 79543 - 79552, 2022. 3. Lizhen Tang And Qusay H. Mahmoud, “A Deep Learning-Based Framework for Phishing Website Detection,” Volume 10, pp. 1509 - 1521, 2022. 4. Nguyet Quang Do, Ali Selamat, Ondrej Krejcar Enrique Herrera-Viedma And Hamido Fujita, “Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions,” Volume 10, pp. 36429 - 36463, 2022. 5. Lakshmana Rao Kalabarige, Routhu Srinivasa Rao, Alwyn R. Pais, And Lubna Abdelkareim Gabralla, “A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble LearningModel to Detect Phishing Websites”, Volume 11, pp. 71180 - 71193, 2023. 6. S. W. B. Tan, P. K. Naraharisetti, S. K. Chin, and L. Y. Lee, ``Simple visual-aided automated titration using the Python programming language,''J. Chem. Educ., vol. 97, no. 3, pp. 850_854, Mar. 2020. 7. A.Al-Ani, M. Anbar, A. K. Al-Ani, andI.H.Hasbullah,``DHCPv6Auth:A mechanism to improve DHCPv6 authentication and privacy,'' Sadhana,Acad. Proc. Eng. Sci., vol. 45, no. 1, Dec. 2020, doi: 10.1007/S12046-019-1244-4. 8. A.K. Al-Ani, M. Anbar, A. Al-Ani, and D. R. Ibrahim,``Matchprevention technique against Denial-of-Service attack on address resolution and duplicate address detection processes in IPv6 link-local network,'' IEEE Access, vol. 8, pp. 27122_27138, 2020. Accessed:Mar. 31, 2022. 9. M. Al-Sarem, F. Saeed, A. Alsaeedi, W. Boulila, and T. Al-Hadhrami,``Ensemble methods for instance-based Arabic language authorship attri-bution,'' IEEE Access, vol. 8, pp. 17331_17345, 2020. 10. X. Niu, J. Ma, Y. Wang, J. Zhang, H. Chen, and H. Tang, ``A novel decomposition-ensemble learning model based on ensemble empirical mode decomposition and recurrent neural network for landslide displacement prediction,'' Appl. Sci., vol. 11, no. 10, p. 4684, May 2021. 11. L. Tang and Q. H. Mahmoud, ``A survey of machine learning-based solu-tions for phishing website detection,'' Mach. Learn. Knowl. Extraction,vol. 3, no. 3, pp. 672_694, Aug. 2021. 12. Phishing Activity Trends Report 1st Quarter 2021.APWG. Accessed: Oct. 20, 2021. 13. M. A. El-Rashidy, ``A smart model for web phishing detection based on new proposed feature selection technique,'' Menou_a J. Electron.Eng. Res., vol. 30, no. 1, pp. 97_104, Jan. 2021. 14. B. B. Gupta, K. Yadav, I. Razzak, K. Psannis, A. Castiglione, and X. Chang, ``A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment,'' Comput. Commun.,vol. 175, pp. 47_57, Jul. 2021. 15. E. Gandotra and D. Gupta, ``Improving spoofed website detection usingmachine learning,'' Cybern. Syst., vol. 52, no. 2, pp. 169_190, Oct. 2020. 16. R. Zaimi, M. Ha_di, and M. Lamia, ``Survey paper: Taxonomy of website anti-phishing solutions,'' in Proc. 7th Int. Conf. Social Netw. Anal., Manage. Secur. (SNAMS), Dec. 2020. 17. Odeh, I.Keshta, and E. Abdelfattah, ``Machine LearningTechniques fordetection of website phishing: A review for promises and challenges,'' in Proc. IEEE 11th Annu. Comput. Commun. Workshop Conf. (CCWC),Jan. 2021. 18. L. Tang and Q. H. Mahmoud, ``A survey of machine learning-based solutions for phishing website detection,'' Mach. Learn. Knowl. Extraction, vol. 3, no. 3, pp. 672_694, Aug. 2021 19. E. S. Aung, C. T. Zan, and H. Yamana. A Survey of URL- Based Phishing Detection. Accessed: Mar. 22, 2022. 20. E. Benavides, W. Fuertes, S. Sanchez, and M. Sanchez, ``Classi_cation of phishing attack solutions by employing deep learning techniques: A systematic literature review,'' in Developments and Advances in Defense and Security. Singapore: Springer, 2020.Security. Singapore: Springer, 2020.