.png){kind=icon}
Return
Content-aware malicious webpage detection using convolutional neural network
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-023-15559-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Malicious websites often install malware on user devices to gather user information or to disrupt device operations, violate user privacy, or adversely affect company interests. Many commercial tools are available to prevent malicious webpages from accessing devices; however, current versions of these tools may become useless as soon as a new generation of malware is released. In this study, a content-aware malicious webpage detection (CAMD) method was developed; this CAMD method can verify whether a webpage is malicious by applying a novel webpage contextual visualization process, which retrieves the critical codes of webpages, transforms those codes into one-dimensional grayscale images, and applies convolutional neural networks to detect any malicious webpages. To verify the feasibility of proposed CAMD, 50000 normal and 50000 malicious webpages from the VirusTotal website were used. The results indicated that the proposed CAMD achieved an accuracy of > 98%.
Keywords:
Content awareness
Convolutional neural network (CNN)
Malicious webpages
Webpage contextual visualization
Journal
IF:
3
Papers:
2.0W
Citations:
3.2W
Organization
Cited Papers
Parameterization of an electrical battery model for dynamic system simulation in electric vehicles
.png){kind=icon}
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSaveDynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks
PREAI
ShareSaveShareSave
View more