Detection of Phishing Websites Using Machine Learning | Python Final Year IEEE Project.
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📌Project Title: Detection of Phishing Websites Using Machine Learning.
💡Implementation Code: Python.
🔬Algorithm / Model Used: Gradient Boosting Classifier.
🌐Web Framework: Flask.
🖥️Frontend: HTML, CSS, JavaScript.
💰Cost (In Indian Rupees): Rs.3000/.
IEEE Base paper Abstract:
Criminals seeking sensitive information construct illegal clones of actual websites and e-mail accounts. The e-mail will be made up of real firm logos and slogans. When a user clicks on a link provided by these hackers, the hackers gain access to all of the user's private information, including bank account information, personal login passwords, and images. Random Forest and Decision Tree algorithms are heavily employed in present systems, and their accuracy has to be enhanced. The existing models have low latency. Existing systems do not have a specific user interface. In the current system, different algorithms are not compared. Consumers are led to a faked website that appears to be from the authentic company when the e-mails or the links provided are opened. The models are used to detect phishing Websites based on URL significance features, as well as to find and implement the optimal machine learning model. Logistic Regression, Multinomial Naive Bayes, and XG Boost are the machine learning methods that are compared. The Logistic Regression algorithm outperforms the other two.
REFERENCE:
Dr.G.K.Kamalam, Dr.P.Suresh, R.Nivash, A.Ramya, G.Raviprasath, “Detection of Phishing Websites Using Machine Learning”, 2022 International Conference on Computer Communication and Informatics (ICCCI - 2022), IEEE Conference, 2022.
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