# Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security

arXiv: https://arxiv.org/abs/2410.01016 · HTML: https://mlsanity.com/paper/2410.01016/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 89 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions](https://mlsanity.com/paper/1808.02924/) (2018)
- [Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges](https://mlsanity.com/paper/2201.04736/) (2022)
- [Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations](https://mlsanity.com/paper/2202.11917/) (2022)
- [Fog Computing Approaches in Smart Cities: A State-of-the-Art Review](https://mlsanity.com/paper/2011.14732/) (2020)
- [IoT Behavioral Monitoring via Network Traffic Analysis](https://mlsanity.com/paper/2001.10632/) (2020)
- [Comparative Analysis of Gradient-Based Optimization Techniques Using Multidimensional Surface 3D Visualizations and Initial Point Sensitivity](https://mlsanity.com/paper/2409.04470/) (2024)
- [A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks](https://mlsanity.com/paper/1504.03976/) (2015)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin](https://mlsanity.com/paper/1708.09790/) (2017)
- [Computational Intelligence based Intrusion Detection Systems for Wireless Communication](https://mlsanity.com/paper/2105.03204/) (2021)

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

- [PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security](https://mlsanity.com/paper/2503.00331/) (2025)

## Abstract (arXiv)

Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. The increase in cyber-attacks that target IoT networks poses a considerable vulnerability and threat to the privacy, security, functionality, and availability of critical systems, which leads to operational disruptions, financial losses, identity thefts, and data breaches. To efficiently secure IoT devices, real-time detection of intrusion systems is critical, especially those using machine learning to identify threats and mitigate risks and vulnerabilities. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security, concentrating on real-time responsiveness, detection accuracy, and algorithm efficiency. Key studies were reviewed from all well-known academic databases, and a taxonomy was provided for the existing approaches. This review also highlights existing research gaps and outlines the limitations of current IoT security frameworks to offer practical insights for future research directions and developments.