Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns

Tiberiu Boros; Marius Barbulescu

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Paper

Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns

Topics: Intrusion and detection techniques; Machine Learning and Deep Learning Approaches Data Analytics; Modeling, Experiments, Sharing Technologies & Platforms; Security, Privacy and Trust

In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security IoTBDS - Volume 1, 185-190, 2024 , Angers, France

Authors: Tiberiu Boros ¹ and Marius Barbulescu ²

Affiliations: ¹ Security Coordination Center, Adobe Systems, Bucharest, Romania ; ² Cloudops, CES Security Solutions, Adobe Systems, Bucharest, Romania

Keyword(s): Machine Learning, Statistical Modeling, Multi-Key Store, Access Pattern, Anomaly Detection.

Abstract: Anomaly detection in datasets with massive amounts of sparse data is not a trivial task, given that working with high intake data in real-time requires careful design of the algorithms and data structures. We present a hybrid statistical modeling strategy which combines an effective data structure with a neural network for Gaussian Process Modeling. The network is trained in a residual learning fashion, which enables learning with less parameters and in fewer steps.

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Paper citation in several formats:

Boros, T. and Barbulescu, M. (2024). Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-699-6; ISSN 2184-4976, SciTePress, pages 185-190. DOI: 10.5220/0012621300003705

@conference{iotbds24,
author={Tiberiu Boros and Marius Barbulescu},
title={Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2024},
pages={185-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012621300003705},
isbn={978-989-758-699-6},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns
SN - 978-989-758-699-6
IS - 2184-4976
AU - Boros, T.
AU - Barbulescu, M.
PY - 2024
SP - 185
EP - 190
DO - 10.5220/0012621300003705
PB - SciTePress