MALICIOUS WEBSITE DETECTION USING INTERMEDIATE REPRESENTATIONS
개요
출원인
Email Veritas Security Technologies Inc.
발명자
William Ryan Briguglio; Issa Traore; Marcelo Luiz Brocardo
IPC 분류
CPC 분류
Websites are classified based on intermediate representations of the associated source code using a machine learning model applied to a set of intermediate representations from websites having predetermined classifications. The use of intermediate representations can provide a machine independent classifier that does not required use of lists of websites known to be malicious. The intermediate representation-based classifier can be combined with URL and HTML based classifiers, including classifiers that incorporate URLs that are both statically and dynamically linked.
원문 (중국어)
Websites are classified based on intermediate representations of the associated source code using a machine learning model applied to a set of intermediate representations from websites having predetermined classifications. The use of intermediate representations can provide a machine independent classifier that does not required use of lists of websites known to be malicious. The intermediate representation-based classifier can be combined with URL and HTML based classifiers, including classifiers that incorporate URLs that are both statically and dynamically linked.