Intelligent Malicious URL Detection with Feature Analysis

EasyChair Preprint no. 3940

Abstract

The Website security is an important issue that must be pursued to protect Internet service users. Traditionally, blacklists of malicious websites are maintained, but they do not help in the detection of new malicious websites. This work proposes a machine learning architecture for intelligent detecting malicious URLs. Forty-one features of malicious URLs are extracted from the data types and processes of domain, Alexa and obfuscation. ANOVA (ANalysis Of Variance) test and XGBoost (eXtreme Gradient Boosting) algorithm are used to extract the 17 most important features for analyzing malicious URL. The dataset including 13027 benign URLs and 13027 malicious URLs is used to build the XGBoost-based malicious URL detector, which has a detection accuracy of more than 99%.

Keyphrases: Artificial Intelligence, feature analysis, JavaScript Detection, Malicious URL

@Booklet{EasyChair:3940,
  author = {Yu-Chen Chen and Yi-Wei Ma and Jiann-Liang Chen},
  title = {Intelligent Malicious URL Detection with Feature Analysis},
  howpublished = {EasyChair Preprint no. 3940},

  year = {EasyChair, 2020}}