| Autor: |
Santoshi Kumari Machikuri, Keerthi Priya Chiguru, Bhavya Gondhi, Neha Haridas, Awasthi Monisha, Tripathi Surendra |
| Jazyk: |
English<br />French |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
E3S Web of Conferences, Vol 430, p 01037 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202343001037 |
| Popis: |
The objective of paper is to detect phishing URLs using machine learning algorithms. Phishing is a fraudulent activity that involves tricking users into giving away sensitive information, such as passwords and credit card numbers, by impersonating legitimate websites. The main objective of this work is to build a model that can accurately detect viable phishing URLs and classify them as either legitimate or fraudulent. This will help to prevent users from falling victim to phishing attacks and protect their personal information. The model will be trained on a large dataset of annotated URLs and will be optimised for high accuracy and low false positive rates. The paper consists of two datasets in which one of the dataset consists of phishing URLs and other datasets consist of features of URLs. The performance of the phishing detection model will be evaluated using various metrics, such as precision, recall, and F1 score. We will also conduct an in-depth analysis of the results and discuss the effectiveness of the approach. This work aims to build a robust model for phishing URL detection using machine learning algorithms. Future enhancements to this work could include incorporating more advanced feature extraction techniques, exploring the use of deep learning models, and expanding the dataset to include more diverse types of URLs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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