Phishing Website Detection

Phishing attacks are a major concern in today’s digital world. Cybercriminals often design websites that appear similar to trusted websites and use them to collect users’ confidential information. If users enter their details on such websites, they may experience problems such as stolen passwords, financial fraud, identity theft, or misuse of personal information.   The proposed Phishing Website Detection system can help identify suspicious websites by examining different characteristics of a website. These characteristics may include the URL structure, domain information, use of special characters, webpage content, and other unusual patterns. After analyzing these features, the system can classify a website as either legitimate or phishing. Machine learning can make the detection process more useful by identifying patterns from previously collected website data.   One of the useful features of a phishing detection system is its ability to alert users before they share sensitive information with a suspicious website. Such early detection can help users avoid fraudulent websites and reduce the possibility of becoming victims of online scams. It can also contribute to safer browsing practices and improve users’ awareness of cybersecurity threats.   However, phishing techniques continue to change as attackers develop new methods for creating realistic-looking fraudulent websites. Therefore, detection systems should be regularly improved by using updated datasets and newer detection techniques. Along with automated security tools, user awareness is also necessary because safe browsing habits play an important role in preventing phishing attacks. In conclusion, Phishing Website Detection can support a safer online environment by identifying suspicious websites at an early stage. Combining automated detection, machine learning, updated security methods, and user awareness can help reduce the risks associated with phishing and protect users from common online threats.

[1]     Y. Zhang, J. I. Hong, and L. F. Cranor, “CANTINA: A content-based approach to detecting phishing web sites,” in Proc. 16th Int. Conf. World Wide Web (WWW), 2007. DOI: 10.1145/1242572.1242669   [2]     G. Xiang, J. Hong, C. P. Rose, and L. Cranor, “CANTINA+: A feature-rich machine learning framework for detecting phishing web sites,” in Proc. 5th ACM Symp. Information, Computer and Communications Security, 2010. DOI: 10.1145/1755688.1755698 [3]     R. M. Mohammad, F. Thabtah, and L. McCluskey, “Predicting phishing websites based on self-structuring neural network,” Neural Computing and Applications, vol. 25, pp. 443–458, 2014. DOI: 10.1007/s00521-013-1490-z   [4]     R. M. Mohammad, F. Thabtah, and L. McCluskey, “Intelligent rule-based phishing websites classification,” IET Information Security, vol. 8, no. 3, pp. 153–160, 2014. DOI: 10.1049/iet-ifs.2013.0203   [5]     S. Sheng et al., “An empirical analysis of phishing blacklists,” in Proc. 6th Conf. Email and Anti-Spam (CEAS),\ 2009.Open paper   [6]     L. Tang and Q. H. Mahmoud, “A survey of machine learning-based solutions for phishing website detection,” Machine Learning and Knowledge Extraction, vol. 3, no. 3, pp. 672–694, 2021. DOI: 10.3390/make3030034   [7]     A. Aljofey et al., “A systematic literature review on phishing website detection techniques,” Journal of King Saud University – Computer and Information Sciences, 2023. DOI: 10.1016/j.jksuci.2023.01.004   [8]     M. Moghimi and A. Y. Varjani, “New rule-based phishing detection method,” Expert Systems with Applications, vol. 53, pp. 231–246, 2016. DOI: 10.1016/j.eswa.2016.08.048   [9]     A. K. Jain and B. B. Gupta, “A machine learning based approach for phishing detection using hyperlinks information,” Journal of Ambient Intelligence and Humanized     Computing,       2018. DOI: 10.1007/s12652-018-0816-z   [10]  R. S. Rao and A. R. Pais, “Detection of phishing websites using an efficient feature-based machine learning framework,” Neural Computing and Applications,       2019. DOI: 10.1007/s00521-019-04268-7