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

Tiberiu Boros, Marius Barbulescu

2024

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.

Paper Citation

in Harvard Style

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 - Volume 1: IoTBDS; ISBN 978-989-758-699-6, SciTePress, pages 185-190. DOI: 10.5220/0012621300003705

in Bibtex Style

@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 - Volume 1: IoTBDS},
year={2024},
pages={185-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012621300003705},
isbn={978-989-758-699-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns
SN - 978-989-758-699-6
AU - Boros T.
AU - Barbulescu M.
PY - 2024
SP - 185
EP - 190
DO - 10.5220/0012621300003705
PB - SciTePress