Towards a Secure and Intelligent Access Control Policy Adapter for Big Data Environment

El Mostapha Chakir, Marouane Hachimi, Marouane Hachimi, Mohammed Erradi

2024

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 model, 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.

Paper Citation

in Harvard Style

Mostapha Chakir E., Hachimi M. and Erradi M. (2024). Towards a Secure and Intelligent Access Control Policy Adapter for Big Data Environment. In Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-709-2, SciTePress, pages 345-356. DOI: 10.5220/0012860800003767

in Bibtex Style

@conference{secrypt24,
author={El Mostapha Chakir and Marouane Hachimi and Mohammed Erradi},
title={Towards a Secure and Intelligent Access Control Policy Adapter for Big Data Environment},
booktitle={Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2024},
pages={345-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012860800003767},
isbn={978-989-758-709-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Towards a Secure and Intelligent Access Control Policy Adapter for Big Data Environment
SN - 978-989-758-709-2
AU - Mostapha Chakir E.
AU - Hachimi M.
AU - Erradi M.
PY - 2024
SP - 345
EP - 356
DO - 10.5220/0012860800003767
PB - SciTePress