Open Access

ARTICLE

A Fast Detection Method of Network Crime Based on User Portrait

Yabin Xu^1,2,*, Meishu Zhang², Xiaowei Xu³

1 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing, 100101, China
2 Computer School, Beijing Information Science and Technology University, Beijing, 100101, China
3 Department of Information Science, University of Arkansas at Little Rock, Little Rock, Arkansas, 72204, USA

* Corresponding Author: Yabin Xu. Email:

Journal of Information Hiding and Privacy Protection 2021, 3(1), 17-28. https://doi.org/10.32604/jihpp.2021.017497

Received 01 January 2021; Accepted 29 March 2021; Issue published 21 April 2021

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.

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Citations