User abnormal behaviour sequence detection method based on Markov chain and SVDD

In order to solve the problem of insufficient use of sequence information and low detection efficiency of traditional anomaly detection methods, this paper introduces Markov chain into user behaviour sequence detection, and proposes a description based on Markov chain and support vector data field (...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2019-05, Vol.267 (4), p.42061
Main Authors: Zou, Shengyuan, Chang, Chaowen, Han, Peisheng
Format: Article
Language:English
Subjects:
Anomalies
Markov analysis
Markov chains
User behavior
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to solve the problem of insufficient use of sequence information and low detection efficiency of traditional anomaly detection methods, this paper introduces Markov chain into user behaviour sequence detection, and proposes a description based on Markov chain and support vector data field ( SVDD) User Behaviour Sequence Detection Method (ASDMS), which first uses the Markov chain to accurately quantify the user behaviour sequence, then constructs the user's normal behaviour sequence model based on the support vector data field description model, and identifies the user anomaly behaviour. The experimental results show that the ASDMS method has better performance and timeliness than the traditional abnormal behaviour detection method.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/267/4/042061