| _version_ | 1866901746720178176 |
|---|---|
| author | K.RISHIKA REDDY V.MANIDEEP P.HARSHAVARDHAN RAJU |
| author_facet | K.RISHIKA REDDY V.MANIDEEP P.HARSHAVARDHAN RAJU |
| contents | <div>ABSTRACT</div> <div>Phishing attacks continue to be a major cybersecurity concern, targeting unsuspecting users by mimicking trusted websites to steal sensitive information such as login credentials, banking details, and personal data. Traditional detection techniques, which often rely on blacklists or manually engineered features, are limited in their ability to detect new and sophisticated phishing attempts. This project introduces an advanced phishing website detection system using deep learning algorithms to automatically learn complex patterns and features from large datasets of website data. The system processes various inputs including URL characteristics, HTML content, and webpage behavior to distinguish between legitimate and phishing websites. The deep learning model is trained and evaluated on a labeled dataset to ensure high accuracy, precision, and recall. Furthermore, explainability techniques are integrated into the system using SHAP (Shapley Additive explanations) to interpret the model’s predictions and enhance trust in its decisions. This approach aims to offer a more intelligent, adaptable, and transparent solution for combating phishing threats in real time.</div> <div>Keywords: Phishing detection, deep learning, cybersecurity, URL phishing and cyber-attack detection</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15397539 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PHISHING WEBSITE DETECTION USING DEEP LEARNING K.RISHIKA REDDY V.MANIDEEP P.HARSHAVARDHAN RAJU <div>ABSTRACT</div> <div>Phishing attacks continue to be a major cybersecurity concern, targeting unsuspecting users by mimicking trusted websites to steal sensitive information such as login credentials, banking details, and personal data. Traditional detection techniques, which often rely on blacklists or manually engineered features, are limited in their ability to detect new and sophisticated phishing attempts. This project introduces an advanced phishing website detection system using deep learning algorithms to automatically learn complex patterns and features from large datasets of website data. The system processes various inputs including URL characteristics, HTML content, and webpage behavior to distinguish between legitimate and phishing websites. The deep learning model is trained and evaluated on a labeled dataset to ensure high accuracy, precision, and recall. Furthermore, explainability techniques are integrated into the system using SHAP (Shapley Additive explanations) to interpret the model’s predictions and enhance trust in its decisions. This approach aims to offer a more intelligent, adaptable, and transparent solution for combating phishing threats in real time.</div> <div>Keywords: Phishing detection, deep learning, cybersecurity, URL phishing and cyber-attack detection</div> |
| title | PHISHING WEBSITE DETECTION USING DEEP LEARNING |
| url | https://doi.org/10.5281/zenodo.15397539 |