Name
Title | ||
|---|---|---|
A machine learning based approach for phishing detection using hyperlinks information |
Abstract | ||
|---|---|---|
This paper presents a novel approach that can detect phishing attack by analysing the hyperlinks found in the HTML source code of the website. The proposed approach incorporates various new outstanding hyperlink specific features to detect phishing attack. The proposed approach has divided the hyperlink specific features into 12 different categories and used these features to train the machine learning algorithms. We have evaluated the performance of our proposed phishing detection approach on various classification algorithms using the phishing and non-phishing websites dataset. The proposed approach is an entirely client-side solution, and does not require any services from the third party. Moreover, the proposed approach is language independent and it can detect the website written in any textual language. Compared to other methods, the proposed approach has relatively high accuracy in detection of phishing websites as it achieved more than 98.4% accuracy on logistic regression classifier. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12652-018-0798-z | Journal of Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Cyber security,Phishing attack,Hyperlink,Social engineering,Website,Machine learning | Phishing,Source code,Computer science,Social engineering (security),Third party,Artificial intelligence,Hyperlink,Statistical classification,Classifier (linguistics),Phishing detection,Machine learning | Journal |
Volume | Issue | ISSN |
10.0 | SP5.0 | 1868-5145 |
Citations | PageRank | References |
3 | 0.42 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ankit Kumar Jain | 1 | 81 | 7.77 |
| B. B. Gupta | 2 | 12 | 1.24 |