# A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems

arXiv: https://arxiv.org/abs/2210.01985 · HTML: https://mlsanity.com/paper/2210.01985/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [Real-Time Machine Learning: The Missing Pieces](https://mlsanity.com/paper/1703.03924/) (2017)

## Built on by (most cited first, 8 of 8)

- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines](https://mlsanity.com/paper/2510.23408/) (2025)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System](https://mlsanity.com/paper/2511.08491/) (2025)
- [A Multi-Step Comparative Framework for Anomaly Detection in IoT Data Streams](https://mlsanity.com/paper/2505.16872/) (2025)
- [An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2609.23097/) (2026)

## Abstract (arXiv)

Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.