Abstract
Phishing involves attempts to trick the user by extracting crucial important information that should otherwise not be leaked. Data like bank account numbers, social media accounts, company revenue reports, online transactions secrets are some of the examples that an attacker hopes to extract by exploiting the vulnerability of the user. In today’s day and age, it is very important to warn the populace against such attacks and provide relevant security awareness. Through our research, we aim to provide a brief study and novel insights into how machine learning and image visualization can be used to detect phishing webpages using a two-part implementation technique. The first part involves an analysis and overview of different machine learning classifiers across different datasets and features to find the most relevant technique to detect phishing websites efficiently. The second part introduces a novel approach of how discrete Fourier transform and image comparison can be used to compare suspicious webpages with real ones. This study aims at providing a new direction for researchers to understand how machine learning and automation can be inculcated in phishing detection to avoid any data breach.
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References
Mahajan, R., Siddavatam, I.: Phishing website detection using machine learning algorithms. Int. J. Comput. Appl. 181, 45–47 (2018). https://doi.org/10.5120/ijca2018918026
James, J., Sandhya, L., Thomas, C.: Detection of phishing URLs using machine learning techniques, 304–309 (2013). https://doi.org/10.1109/ICCC.2013.6731669
Nguyen, L. To, B., Nguyen, H., Nguyen, M.: Detecting phishing web sites: a heuristic URL-based approach, 597–602 (2013). https://doi.org/10.1109/ATC.2013.6698185
Shahrivari, V., Darabi, M., Izadi, M.: Phishing detection using machine learning techniques (2020). arXiv abs/2009.11116
Yi, P., Guan, Y., Zou, F., Yao, Y., Wang, W., Zhu, T.: Web phishing detection using a deep learning framework. Wirel. Commun. Mob. Comput. 2018, 1–9 (2018). https://doi.org/10.1155/2018/4678746
Mehanović, D., Kevrić, J.: Phishing website detection using machine learning classifiers optimized by feature selection. Traitement du Signal 37(4), 563–569 (2020). https://doi.org/10.18280/ts.370403
Kulkarni, A., Brown, L.L., III.: Phishing websites detection using machine learning. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 10(7) (2019)
Zhang, N., Yuan, Y.: Phishing detection using neural network - CS229 lecture notes, 2012 - cs229.stanford.edu (2012)
Selvakumari, M., et al.: Phishing website detection using machine learning and deep learning techniques. J. Phys.: Conf. Ser. 1916, 012169 (2021)
Akila, D.: Phishing websites detection using machine learning 8, 111–114 (2019). https://doi.org/10.35940/ijrte.B1018.0982S1119
Shikalgar, S., Sawarkar, S.D., Narwane, S.: Detection of URL based phishing attacks using machine learning. Int. J. Eng. Res. Tech. (IJERT) 08(11) (2019)
Wikipedia, https://en.wikipedia.org/wiki/Logistic_regression
PluralSight, https://www.pluralsight.com/guides/classification-keras
Sara, U., Akter, M., Uddin, M.: Image quality assessment through FSIM, SSIM, MSE and PSNR-A comparative study. J. Comput. Commun. 7, 8–18 (2019). https://doi.org/10.4236/jcc.2019.73002
Shekokar, N., Shah, C., Mahajan, M., Rachh, S.: An ideal approach for detection and prevention of phishing attacks. Proc. Comput. Sci. 49, 82–91 (2015). https://doi.org/10.1016/j.procs.2015.04.230
Nussbaumer, H.J.: The fast fourier transform. In: Fast Fourier Transform and Convolution Algorithms. Springer Series in Information Sciences, vol. 2. Springer, Berlin (1981). https://doi.org/10.1007/978-3-662-00551-4_4
Cooley, J.W., Lewis, P.A.W., Welch, P.D.: The fast fourier transform and its applications. IEEE Trans. Educ. 12(1), 27–34 (1969). https://doi.org/10.1109/TE.1969.4320436
Flynn, J.R., Ward, S., Abich, J., Poole, D.: Image quality assessment using the SSIM and the just noticeable difference paradigm. In: Harris, D. (Ed.), Engineering psychology and cognitive ergonomics. Understanding human cognition. EPCE 2013. Lecture Notes in Computer Science, vol. 8019. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-39360-0_3
Sungheetha, A., Sharma, R.: 3D image processing using machine learning based input processing for man-machine interaction. J. Innovat. Image Process. 3(1), 1–6 (2021). https://doi.org/10.36548/jiip.2021.1.001
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Sanghavi, P., Kunchapu, A., Kulkarni, A., Solani, D., Anson, A. (2022). Novel Approach to Phishing Detection Using ML and Visual Similarity. In: Chen, J.IZ., Wang, H., Du, KL., Suma, V. (eds) Machine Learning and Autonomous Systems. Smart Innovation, Systems and Technologies, vol 269. Springer, Singapore. https://doi.org/10.1007/978-981-16-7996-4_9
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