FLASK WEBSITE FOR DETECTING PHISHING WEBSITES USING MACHINE LEARNING

Registration ID: IJNRD_191193

Published ID: IJNRD2304258

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Nandhitha S, Siva S, Meganasundaram R, Billdass Santhosm I, John Thiagarajan G

Phishing, cybersecurity, data mining, encryption, Random Forest Classifier, Flask Framework.

Phishing attacks are a prevalent security threat, and detecting and preventing such attacks is crucial to safeguarding sensitive information. By performing a phishing attack the attacker can get hold of the victim’s personal details including login credentials, and credit card details, and perform some fraudulent activities. To address this issue, our proposed method makes use of machine learning techniques and uses some classification algorithms, such as K-nearest neighbor, decision trees, Random Forest and Ada Boost to identify phishing URLs. For this we use a dataset that consists of 38,625 data of which 16,252 data are legitimate and are taken from alexa.com and 22,373 data are phishing taken from phishtank.com. The data pre-processing is performed on the data by applying techniques such as under-sampling and over-sampling, and as a part of feature extraction 12 features are selected and the model is trained on these data, then the model is tested using the test data. Finally, we evaluate the performance of each algorithm using performance metrics such as accuracy, precision, f1 score, and recall. After evaluating the algorithms, we save the best-performing model in a pickle file. Our results indicate that the Random Forest classifier achieved the highest accuracy, with a score of 96.56%. Using the Flask framework, we developed a web application, where the user can check the legitimacy of the URL. Once the user enters the URL in the search bar provided, then our model will predict whether the URL is legitimate or a phishing attempt, and if it is a phishing URL, a warning message will be displayed to the user. This approach will help prevent users from falling victim to phishing attacks and safeguard their sensitive information.

Published Paper   E-Certificate

Nandhitha S, Siva S, Meganasundaram R, Billdass Santhosm I, & John Thiagarajan G (April-2023). FLASK WEBSITE FOR DETECTING PHISHING WEBSITES USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), c446-c451. https://ijnrd.org/papers/IJNRD2304258.pdf

Volume 8 Issue 4, April-2023

Pages : c446-c451

Paper Reg. ID: IJNRD_191193

Published Paper Id: IJNRD2304258

Downloads: 000121996

Research Area: Information Technology 

Author Type: Indian Author

Country: The Nilgiris, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304258.pdf

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