Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection
Considering the fatality of phishing attacks, the data-driven approach using massive URL observations has been verified, especially in the field of cyber security. On the other hand, the supervised learning approach relying on known attacks has limitations in terms of robustness against zero-day phi...
Main Authors: | Seok-Jun Bu, Sung-Bae Cho |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/10/12/1492 |
Similar Items
-
Dataset of suspicious phishing URL detection
by: Maruf Ahmed Tamal, et al.
Published: (2024-03-01) -
A Survey of Intelligent Detection Designs of HTML URL Phishing Attacks
by: Sultan Asiri, et al.
Published: (2023-01-01) -
AntiPhishStack: LSTM-Based Stacked Generalization Model for Optimized Phishing URL Detection
by: Saba Aslam, et al.
Published: (2024-02-01) -
Phishing or Not Phishing? A Survey on the Detection of Phishing Websites
by: Rasha Zieni, et al.
Published: (2023-01-01) -
An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL
by: Ali Aljofey, et al.
Published: (2020-09-01)