An enhanced deep learning‐based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders
Abstract Phishing attacks have become one of the powerful sources for cyber criminals to impose various forms of security attacks in which fake website Uniform Resource Locators (URL) are circulated around the Internet community in the form of email, messages etc., in order to deceive users, resulti...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-05-01
|
| Series: | IET Information Security |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ise2.12106 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Phishing attacks have become one of the powerful sources for cyber criminals to impose various forms of security attacks in which fake website Uniform Resource Locators (URL) are circulated around the Internet community in the form of email, messages etc., in order to deceive users, resulting in the loss of their valuable assets. The phishing URLs are predicted using several blacklist‐based traditional phishing website detection techniques. However, numerous phishing websites are frequently constructed and launched on the Internet over time; these blacklist‐based traditional methods do not accurately predict most phishing websites. In order to effectively identify malicious URLs, an enhanced deep learning‐based phishing detection approach has been proposed by integrating the strength of Variational Autoencoders (VAE) and deep neural networks (DNN). In the proposed framework, the inherent features of a raw URL are automatically extracted by the VAE model by reconstructing the original input URL to enhance phishing URL detection. For experimentation, around 1 lakh URLs were crawled from two publicly available datasets, namely ISCX‐URL‐2016 dataset and Kaggle dataset. The experimental results suggested that the proposed model has reached a maximum accuracy of 97.45% and exhibits a quicker response time of 1.9 s, which is better when compared to all the other experimented models. |
|---|---|
| ISSN: | 1751-8709 1751-8717 |