<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to detect, and novel approaches utilize machine learning to identify compromised accounts. This paper aims to conduct a literature review on account takeover detection and user behavior analysis within the cybersecurity domain. By exploring these areas, the goal is to combat account takeovers and other fraudulent attempts effectively.</p>
</abstract>

The rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to detect, and novel approaches utilize machine learning to identify compromised accounts. This paper aims to conduct a literature review on account takeover detection and user behavior analysis within the cybersecurity domain. By exploring these areas, the goal is to combat account takeovers and other fraudulent attempts effectively.

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper"}

The rise of online transactions has led to a corresponding increase in online criminal activities. Account takeover attacks, in particular, are challenging to...

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Publié en ligne: 28 sept. 2023

Pages: 102 - 113

Reçu: 05 juil. 2023

Accepté: 18 août 2023

DOI: https://doi.org/10.2478/cait-2023-0027

Mots clés
Machine learning, account takeover, ATO, user behavior analysis, literature review

© 2023 M. Jurišić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper

Publié en ligne: 28 sept. 2023

Pages: 102 - 113

Reçu: 05 juil. 2023

Accepté: 18 août 2023

DOI: https://doi.org/10.2478/cait-2023-0027

Mots clésMachine learning, account takeover, ATO, user behavior analysis, literature review

© 2023 M. Jurišić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Machine learning, account takeover, ATO, user behavior analysis, literature review