@Article{jihpp.2021.017497,
AUTHOR = {Yabin Xu, Meishu Zhang, Xiaowei Xu},
TITLE = {A Fast Detection Method of Network Crime Based on User Portrait},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {17--28},
URL = {http://www.techscience.com/jihpp/v3n1/42327},
ISSN = {2637-4226},
ABSTRACT = {In order to quickly and accurately find the implementer of the network 
crime, based on the user portrait technology, a rapid detection method for users 
with abnormal behaviorsis proposed. This method needs to construct the abnormal 
behavior rule base on various kinds of abnormal behaviors in advance, and 
construct the user portrait including basic attribute tags, behavior attribute tags and 
abnormal behavior similarity tagsfor network users who have abnormal behaviors. 
When a network crime occurs, firstly get the corresponding tag values in all user 
portraits according to the category of the network crime. Then, use the Naive 
Bayesian method matching each user portrait, to quickly locate the most likely 
network criminal suspects. In the case that no suspect is found, all users are 
audited comprehensively through matching abnormal behavior rule base. The 
experimental results show that, the accuracy rate of using this method for fast 
detection of network crimes is 95.9%, and the audit time is shortened to 1/35 of 
that of the conventional behavior audit method.},
DOI = {10.32604/jihpp.2021.017497}
}