Authors:
El Mostapha Chakir
1
;
Marouane Hachimi
1
;
2
and
Mohammed Erradi
3
Affiliations:
1
HENCEFORTH, Rabat, Morocco
;
2
INPT, Rabat, Morocco
;
3
ENSIAS, Mohammed V University, Rabat, Morocco
Keyword(s):
Access Control, Policy Adaptation, Time Series, Big Data, Machine Learning.
Abstract:
In today’s digital landscape, Big Data is crucial for business efficiency and decision-making, but it raises significant Access Control challenges due to its growing scale, complexity, and diversity of user interactions. These challenges include ensuring data integrity, maintaining privacy, and preventing unauthorized access, all of which become increasingly difficult as data volumes and access points expand. In this paper, we propose an approach that combines Time Series Anomaly Detection with Machine Learning (ML) to enable adaptive Access Control policies that dynamically adjust based on detected anomalies and changing user behaviors in Big Data environments. By analyzing collected logs, we extract models of users’ behaviors, which are then utilized to train an ML model specifically designed to identify abnormal behavioral patterns indicative of potential security breaches or unauthorized access attempts. The Access Control Policy Adapter uses the anomalies identified by the ML mo
del, along with static and behavioral anomaly detection techniques, to adjust Access Control policies, thus ensuring that the system remains robust against evolving threats. We validate this approach using a synthetic dataset, and initial results demonstrate the effectiveness of this method, underscoring its potential to significantly enhance data security in complex Big Data ecosystems.
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