<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

Online veröffentlicht: 28. Sept. 2023

Seitenbereich: 102 - 113

Eingereicht: 05. Juli 2023

Akzeptiert: 18. Aug. 2023

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

Schlüsselwörter
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

Online veröffentlicht: 28. Sept. 2023

Seitenbereich: 102 - 113

Eingereicht: 05. Juli 2023

Akzeptiert: 18. Aug. 2023

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

SchlüsselwörterMachine 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.

Schlüsselwörter
Machine learning, account takeover, ATO, user behavior analysis, literature review