Phishing Website Detection using Machine Learning
Phishing attacks, a prevalent and significant form of cybercrime, involve attackers masquerading as reputable entities to deceive individuals into revealing sensitive details such as usernames, passwords, and credit card information. Deceptive websites are commonly used in these attacks, appearin...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
|
| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2063/2/604 http://eprints.intimal.edu.my/2063/3/joit2024_30.pdf http://eprints.intimal.edu.my/2063/ http://ipublishing.intimal.edu.my/joint.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Phishing attacks, a prevalent and significant form of cybercrime, involve attackers masquerading
as reputable entities to deceive individuals into revealing sensitive details such as usernames,
passwords, and credit card information. Deceptive websites are commonly used in these attacks,
appearing legitimate and underscoring the need for individuals and organizations to heighten
their awareness and implement stronger and more advanced detection techniques. By luring
sensitive information through deceptive websites, phishing attacks represent a serious
cybersecurity threat. In this research, the effectiveness of machine learning algorithms,
specifically the Gradient Boosting Classifier, in identifying phishing websites to enhance
accuracy and response time is being assessed. |
|---|