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Detection for user impersonation attacks in mobile social networks based on high-order Markov chains

  • Nanchang Business College of JXAU
    ,
  • University of South-Eastern Norway
    ,
  • Norwegian Research Center NORCE
    ,
  • IDEAS NCBR
Research Output:
Contribution to journal
Article
 Peer-review

Open access

Abstract

In security defense of MSN (MSN), attackers often impersonate themselves as other users, making it difficult to detect network user attacks based on user behavior. Multi-order Markov chains can consider the front-to-back correlation of user behavior, thereby more accurately identifying disguised users. Therefore, this paper proposes a user impersonation attack detection method based on multi-order Markov chains. First, the relevance coefficient method is used to determine the order of the multi-order Markov chain, and by defining appropriate multi-order Markov chain states to capture key features in user behavior, a multi-order Markov chain is established. Then, through the multi-order Markov chain combined with Shell commands, the normal behavior profile of legitimate users is established, and based on this, the probability of occurrence of the state sequence is calculated to complete the detection of userimpersonation attacks. The experimental results show that the similarity between the results of the proposed method and the actual situation in detecting impersonation attacks is more than 97%, indicating that this method can detect MSN user impersonation attacks with high accuracy.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 191-200 (10 pages)

Journal (Volume, Issue Number)

Mobile Networks and Applications (Volume 30, Issue 1-2)

Publication milestones

  • Accepted/In press - 17/03/2025
  • Published - 31/03/2025

Publication status

Published - 31/03/2025

ISSN

1383-469X

Publication IDs

  • handle.net: 10547/626639
  • Scopus: 105001529386

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