Detection of Phishing Website Using Machine Learning Approach

Vilas, Mahajan Mayuri; Ghansham, Kakade Prachi; Jaypralash, Sawant Purva; Shila, Pawar

doi:10.1109/iceeccot46775.2019.9114695

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…The first method evaluates several URL components; the second method assesses a website's authority, determines if it has been introduced or not, and determines who is in charge of it; the third method verifies a website's veracity. [1] In paper "Phishing Attacks Detection using Machine Learning and Deep Learning Models" In this study, the highest correlated features from two distinct datasets were chosen. These features combined contentbased, URL and domainbased features.…”

Section: Literature Surveymentioning

confidence: 99%

Monitoring and Management of Phishing and Malicious Websites using Machine Learning

et al. 2024

IJFMR

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- Phishing internet sites are one of the internet protections issues that focus on human vulnerabilities rather than software program vulnerabilities. It can be defined because of the process of attracting online users to gain their touchy facts which include usernames and passwords. In this paper, we provide a sensible machine for detecting phishing websites. The gadget acts as a further functionality to an internet browser as an extension that routinely notifies the consumer whilst it detects a phishing internet site. The system is based on a device gaining knowledge of approach, particularly supervised mastering. We have decided on the XGBoost method because of its true overall performance in classification. Our focus is to pursue a better overall performance classifier by analyzing the features of phishing websites and choose the better aggregate of them to train the classifier. As a result, we finished our paper with accuracy of 99% accuracy with 48 features.

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Section: Literature Surveymentioning

confidence: 99%

Monitoring and Management of Phishing and Malicious Websites using Machine Learning

et al. 2024

IJFMR

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“…Vilas et al [10] described an approach for detecting websites using ML. The method involved training a ML model to classify websites as phishing or legitimate based on their features, such as the URL structure, the content of the website, and the presence of certain keywords using ML models.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approach for Phishing Attack Detection

Choudhary,

Mhapankar,

Bhddha

et al. 2023

JAIT

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Phishing is the easiest method for gathering sensitive information from unwary people. Phishers seek to get private data including passwords, login information, and bank account details. Cyber security experts are actively seeking for trustworthy and effective ways to identify phishing websites. In order to distinguish between legal and phishing URLs, we used machine learning (ML) technology. In this research work using ML technology extraction and analysis of both types of URLs was performed. Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were used to identify phishing websites. The goal was to identify phishing URLs and determine the most effective ML technique by comparing the accuracy rates of each algorithm. In this, proposed methodology two datasets were used. The accuracy of models was calculated on Phishtank and UCI dataset using kfold, feature selection and hyperparameter tuning method. Performance measures precision, recall and F1-score and Receiver Operating Characteristics (ROC) curve were calculated. RF provided an accuracy of 98.80% and 97.87% on the Phishtank dataset and UCI respectively. Highest precision, recall, F1-score value was 99% each and AUC-ROC value was 99.89% with Phishtank dataset. Validation with other researchers showed better results with proposed methodology. Therefore this methodology can be of help to identify phishing websites.

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