...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal on Semantic Web and information systems >Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study
【24h】

Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study

机译：Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to the study was working and extracting the URL and domain identity feature, abnormal features, HTML and JavaScript features, and domain features as semantic features to detect phishing websites, which makes the process of classification using those semantic features more controllable and more effective. The current study used the machine learning model algorithms to detect phishing websites, and comparisons were made. The authors have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results, 84% and 81% respectively, in comparison with other classifiers.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号