# Detecting Anomalies within Smart Buildings using Do-It-Yourself Internet of Things

arXiv: https://arxiv.org/abs/2210.01840 · HTML: https://mlsanity.com/paper/2210.01840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 56 references that are in the map.
- First posted on arXiv 4 Oct 2022.

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

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities](https://mlsanity.com/paper/2003.13213/) (2020)
- [On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach](https://mlsanity.com/paper/1908.09238/) (2019)

## Built on by (most cited first, 1 of 1)

- [Community-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networks](https://mlsanity.com/paper/2601.05984/) (2026)

## Abstract (arXiv)

Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.