Detecting Malicious URLs via a Keyword-Based Convolutional Gated-Recurrent-Unit Neural Network
With the continuous development of Web attacks, many web applications have been suffering from various forms of security threats and network attacks. The security detection of URLs has always been the focus of Web security. Many web application resources can be accessed by simply entering an URL or...
Main Authors: | Wenchuan Yang, Wen Zuo, Baojiang Cui |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8629082/ |
Similar Items
-
Malicious URL Detection Based on a Parallel Neural Joint Model
by: Jianting Yuan, et al.
Published: (2021-01-01) -
Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem
by: Chun-Ming Lai, et al.
Published: (2020-11-01) -
Malicious URL Detection Based on Associative Classification
by: Sandra Kumi, et al.
Published: (2021-01-01) -
An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset
by: Shen He, et al.
Published: (2021-01-01) -
Using Machine Learning to Detect Malicious URLs
by: Cheng, Aidan
Published: (2017)