# Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on the CICIoMT2024 Dataset

arXiv: https://arxiv.org/abs/2502.11854 · HTML: https://mlsanity.com/paper/2502.11854/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 2 references that are in the map.
- First posted on arXiv 17 Feb 2025.

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

- [Research on CNN-BiLSTM Network Traffic Anomaly Detection Model Based on MindSpore](https://mlsanity.com/paper/2504.21008/) (2025)

## Abstract (arXiv)

The rapid proliferation of Internet of Medical Things (IoMT) devices in healthcare has introduced unique cybersecurity challenges, primarily due to the diverse communication protocols and critical nature of these devices This research aims to develop an advanced, real-time anomaly detection framework tailored for IoMT network traffic, leveraging AI/ML models and the CICIoMT2024 dataset By integrating multi-protocol (MQTT, WiFi), attack-specific (DoS, DDoS), time-series (active/idle states), and device-specific (Bluetooth) data, our study captures a comprehensive range of IoMT interactions As part of our data analysis, various machine learning techniques are employed which include an ensemble model using XGBoost for improved performance against specific attack types, sequential models comprised of LSTM and CNN-LSTM that leverage time dependencies, and unsupervised models such as Autoencoders and Isolation Forest that are good in general anomaly detection The results of the experiment prove with an ensemble model lowers false positive rates and reduced detections.