An approach to detect user behaviour anomalies within identity federations

نویسندگان

چکیده

User and Entity Behaviour Analytics (UEBA) mechanisms rely on statistical techniques Machine Learning to determine when a significant deviation from patterns or trends established as standard for users entities is occurring. These are beneficial within cybersecurity contexts because they allow managers administrators have early alerts warning about potential security incidents. This paper proposes the utilisation of UEBA improve Federated Identity Management (FIM) solutions. The proposed workflow allows Relying Parties identity federations build session fingerprint characterising each user’s behaviour available information. Furthermore, it enables anomaly detection based this fingerprint, integrating raised current management specifications. validated evaluated in real use case web chat application using OpenID Connect management.

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ژورنال

عنوان ژورنال: Computers & Security

سال: 2021

ISSN: ['0167-4048', '1872-6208']

DOI: https://doi.org/10.1016/j.cose.2021.102356