Intelligent Anomaly Detection for Context-Oriented Data Brokerage Systems

Rawaa Al-Wani, Mays Al-Naday

2025

Abstract

Applications of the Internet of Things (IoT) face challenges related to interoperability and heterogeneity due to variations in data representation formats and the absence of connectivity standards across wireless networks. This has led to the emergence of context-oriented data brokering frameworks, with FIWARE being the most widely adopted. However, such frameworks are not able to differentiate malicious from benign data. Consequently, challenges related to data quality persist, and brokering overlays are susceptible to exploitation for the distribution of malicious data assets. We propose a novel Artificial Intelligence (AI) anomaly detection service that communicates with the FIWARE broker via the Fast Application Programming Interface (FastAPI). The system also uses the Publish/Subscribe (Pub/Sub) model of FIWARE to allow networking between brokers to validate data assets before disseminating them. This is to analyze the overhead that anomaly detection introduces as a cost of the solution. The results show that the solution can detect around 95% malicious data, with an approximate overhead of 12% increase in response time.

Paper Citation

in Harvard Style

Al-Wani R. and Al-Naday M. (2025). Intelligent Anomaly Detection for Context-Oriented Data Brokerage Systems. In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-750-4, SciTePress, pages 442-449. DOI: 10.5220/0013478800003944

in Bibtex Style

@conference{iotbds25,
author={Rawaa Al-Wani and Mays Al-Naday},
title={Intelligent Anomaly Detection for Context-Oriented Data Brokerage Systems},
booktitle={Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2025},
pages={442-449},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013478800003944},
isbn={978-989-758-750-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Intelligent Anomaly Detection for Context-Oriented Data Brokerage Systems
SN - 978-989-758-750-4
AU - Al-Wani R.
AU - Al-Naday M.
PY - 2025
SP - 442
EP - 449
DO - 10.5220/0013478800003944
PB - SciTePress