mlsanityData to 7 Oct 2026

An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data

arXiv 2109.13828 ↗First posted 28 Sep 2021cs.LG cs.CRcited by 0

Not on the map. Why?

Abstract (arXiv)

The rapid development in the field of System of Chip (SoC) technology, Internet of Things (IoT), cloud computing, and artificial intelligence has brought more possibilities of improving and solving the current problems. With data analytics and the use of machine learning/deep learning, it is made possible to learn the underlying patterns and make decisions based on what was learned from massive data generated from IoT sensors. When combined with cloud computing, the whole pipeline can be automated, and free of manual controls and operations. In this paper, an implementation of an automated data engineering pipeline for anomaly detection of IoT sensor data is studied and proposed. The process involves the use of IoT sensors, Raspberry Pis, Amazon Web Services (AWS) and multiple machine learning techniques with the intent to identify anomalous cases for the smart home security system.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 35 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

12 of 35 references.

References · 12

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
  3. 2020k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)cited by 56
  4. 2021MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehiclescited by 36
  5. 2021A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streamscited by 28
  6. 2019Tree-based Intelligent Intrusion Detection System in Internet of Vehiclescited by 25
  7. 2021Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunitiescited by 14
  8. 2020Trends in Development of Databases and Blockchaincited by 9
  9. 2020Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approachcited by 9
  10. 2021Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detectioncited by 9
  11. 2020A Transfer Learning Framework for Anomaly Detection Using Model of Normalitycited by 8
  12. 2020DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approachcited by 8

Built on by

Not cited yet.