# IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective

arXiv: https://arxiv.org/abs/2209.08018 · HTML: https://mlsanity.com/paper/2209.08018/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 148 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 28 of 28)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows](https://mlsanity.com/paper/2101.04834/) (2021)
- [Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications](https://mlsanity.com/paper/2008.06701/) (2020)
- [Concept Drift Adaptation by Exploiting Historical Knowledge](https://mlsanity.com/paper/1702.03500/) (2017)
- [Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection](https://mlsanity.com/paper/2008.03297/) (2020)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection](https://mlsanity.com/paper/2008.02327/) (2020)
- [Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems](https://mlsanity.com/paper/2102.10142/) (2021)
- [Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization](https://mlsanity.com/paper/2012.08489/) (2020)
- [Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach](https://mlsanity.com/paper/2012.11325/) (2020)
- [Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection](https://mlsanity.com/paper/2107.11514/) (2021)
- [A fast algorithm for computing distance correlation](https://mlsanity.com/paper/1810.11332/) (2018)
- [Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey](https://mlsanity.com/paper/2004.05289/) (2020)
- [Machine learning and data analytics for the IoT](https://mlsanity.com/paper/2007.04093/) (2020)

## Built on by (most cited first, 16 of 16)

- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [Integrating Large Language Models with Internet of Things Applications](https://mlsanity.com/paper/2410.19223/) (2024)
- [Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection](https://mlsanity.com/paper/2409.03141/) (2024)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission](https://mlsanity.com/paper/2402.02043/) (2024)
- [Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice](https://mlsanity.com/paper/2403.17458/) (2024)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach](https://mlsanity.com/paper/2403.17181/) (2024)
- [Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System](https://mlsanity.com/paper/2511.08491/) (2025)
- [Real-Time Adaptive Anomaly Detection in Industrial IoT Environments](https://mlsanity.com/paper/2601.03085/) (2026)
- [Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices](https://mlsanity.com/paper/2509.01592/) (2025)
- [Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions](https://mlsanity.com/paper/2604.06411/) (2026)
- [Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2607.26467/) (2026)
- [An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2609.23097/) (2026)
- [Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks](https://mlsanity.com/paper/2609.31981/) (2026)
- [Online AutoML: Evaluating Poisoning Attacks on Cost-Utility Adversarial Training Defence Strategy for IoT Networks](https://mlsanity.com/paper/2610.05810/) (2026)

## Abstract (arXiv)

With the wide spread of sensors and smart devices in recent years, the data generation speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges, specifically, effective model selection, design/tuning, and updating, which have brought massive demand for experienced data scientists. Additionally, the dynamic nature of IoT data may introduce concept drift issues, causing model performance degradation. To reduce human efforts, Automated Machine Learning (AutoML) has become a popular field that aims to automatically select, construct, tune, and update machine learning models to achieve the best performance on specified tasks. In this paper, we conduct a review of existing methods in the model selection, tuning, and updating procedures in the area of AutoML in order to identify and summarize the optimal solutions for every step of applying ML algorithms to IoT data analytics. To justify our findings and help industrial users and researchers better implement AutoML approaches, a case study of applying AutoML to IoT anomaly detection problems is conducted in this work. Lastly, we discuss and classify the challenges and research directions for this domain.