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March 27, 2026International Journal of Critical Infrastructures0 citationsOpen Access

Integrating IoT and machine learning for scalable anomaly detection in smart city infrastructure

JXJing Xu

Key Points

  • The aim is to improve the detection of anomalies in smart city infrastructure using IoT and machine learning.
  • Integration of IoT devices for data collection
  • Application of machine learning algorithms to analyze data
  • Focus on handling diverse data types for anomaly detection
  • Enhanced speed in detecting anomalies in IoT networks
  • Improved efficiency in processing varied data types
  • Potential for better responses to security threats in smart cities

Abstract

People all over the world can connect a lot of smart things to the internet of things (IoT).These tools can talk to other tools in the same family without any help from people.The internet of things (IoT) lets us get and look at a lot of data.Many good things could come from this.A lot of data is made when more things join the IoT.You might find strange things after reading this.It has a lot of different kinds of things.Standard ways to keep an eye on hacking threats need to handle and process different kinds of data in different ways.This might not work well for files that have a lot of different parts.But data from more than one kind of network gadget can hold more kinds of data.It will help you find strange things more quickly.

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Cite This Study

Jing Xu (2026) studied this question.

synapsesocial.com/papers/69c620ab15a0a509bde193cbhttps://doi.org/10.1504/ijcis.2026.152499
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