SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

• URL: http://arxiv.org/abs/2511.03661v1
• Date: Wed, 05 Nov 2025 17:20:23 GMT
• Title: SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection
• Authors: Mahek Desai, Apoorva Rumale, Marjan Asadinia,
• Abstract summary: This study proposes a machine learning-driven framework for detecting malicious cyberattacks and identifying faulty device anomalies.<n>Eight machine learning models are evaluated across three learning approaches.<n>The framework has the potential to prevent data breaches, minimize system downtime, and ensure the continuous and safe operation of medical devices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for (1) detecting malicious cyberattacks and (2) identifying faulty device anomalies, leveraging a dataset of 200,000 records. Eight machine learning models are evaluated across three learning approaches: supervised learning (XGBoost, K-Nearest Neighbors (K- NN)), semi-supervised learning (Generative Adversarial Networks (GAN), Variational Autoencoders (VAE)), and unsupervised learning (One-Class Support Vector Machine (SVM), Isolation Forest, Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) Autoencoders). The comprehensive evaluation was conducted across multiple metrics like F1-score, precision, recall, accuracy, ROC-AUC, computational efficiency. XGBoost achieved 99\% accuracy with minimal computational overhead (0.04s) for anomaly detection, while Isolation Forest balanced precision and recall effectively. LSTM Autoencoders underperformed with lower accuracy and higher latency. For attack detection, KNN achieved near-perfect precision, recall, and F1-score with the lowest computational cost (0.05s), followed by VAE at 97% accuracy. GAN showed the highest computational cost with lowest accuracy and ROC-AUC. These findings enhance IoT-enabled healthcare security through effective anomaly detection strategies. By improving early detection of cyber threats and device failures, this framework has the potential to prevent data breaches, minimize system downtime, and ensure the continuous and safe operation of medical devices, ultimately safeguarding patient health and trust in IoT-driven healthcare solutions.

Related papers

• Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection [0.22940141855172033]
  Network Intrusion Systems (NIDS) are essential for securing networks by identifying and mitigating unauthorized activities.<n>This study explores the application of machine learning (ML) methods to improve the NIDS accuracy.
  arXiv  Detail & Related papers  (2025-12-14T15:02:48Z)
• Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison [0.0]
  This study investigates the effectiveness of two unsupervised anomaly detection techniques, Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM)<n>IF consistently outperformed OC-SVM, achieving higher detection accuracy, superior precision, and recall, along with a significantly better F1-score.<n>These findings underscore Isolation Forest's robustness in high-dimensional and imbalanced IoT environments.
  arXiv  Detail & Related papers  (2025-11-26T19:17:29Z)
• Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters [39.9470953186283]
  Continuous electrocardiogram (ECG) monitoring via wearables offers significant potential for early cardiovascular disease (CVD) detection.<n> deploying deep learning models for automated analysis in resource-constrained environments faces reliability challenges due to Out-of-Distribution data.<n>This paper explores Unsupervised Anomaly Detection (UAD) as an independent, upstream filtering mechanism to improve robustness.
  arXiv  Detail & Related papers  (2025-10-30T13:54:37Z)
• Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method [0.0]
  The rapid growth of the Internet of Things has transformed industries by enabling seamless data exchange among connected devices.<n> IoT networks remain vulnerable to security threats such as denial of service (DoS) attacks, anomalous traffic, and data manipulation.<n>This paper proposes an advanced anomaly detection framework with three main phases.
  arXiv  Detail & Related papers  (2025-10-21T22:42:05Z)
• Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning [0.0]
  Intrusion Detection Systems (IDS) are essential for identifying and preventing abnormal network behaviors and malicious activities.<n>This research implements six innovative approaches to enhance IDS performance, including leveraging an autoencoder for dimensional reduction.<n>We are the first to deploy our model on a Jetson Nano, achieving inference times of 0.185 ms for binary classification and 0.187 ms for multiclass classification.
  arXiv  Detail & Related papers  (2025-01-25T16:24:18Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems [0.23408308015481666]
  Our proposed model consists on a combination of convolutional neural network (CNN) and long short-term memory (LSTM) deep learning (DL) models. This fusion facilitates the detection and classification of IoT traffic into binary categories, benign and malicious activities. Our proposed model achieves an accuracy rate of 98.42%, accompanied by a minimal loss of 0.0275.
  arXiv  Detail & Related papers  (2024-05-28T22:12:15Z)
• Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS [48.353590166168686]
  Internet of Things (IoT) networks contain benign traffic far more than abnormal traffic, with some rare attacks. Most existing studies have been focused on sacrificing the detection rate of the majority class in order to improve the detection rate of the minority class. We propose a lightweight framework named S2CGAN-IDS to expand the number of minority categories in both data space and feature space.
  arXiv  Detail & Related papers  (2023-06-06T14:19:23Z)
• Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems [2.088376060651494]
  This paper presents preliminary results on evaluating the robustness of ML-based anomaly detection methods in safety-critical CPS. We test the hypothesis of whether integrating the domain knowledge (e.g., on unsafe system behavior) with the ML models can improve the robustness of anomaly detection without sacrificing accuracy and transparency.
  arXiv  Detail & Related papers  (2022-04-20T02:02:56Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Feature Analysis for ML-based IIoT Intrusion Detection [0.0]
  Powerful Machine Learning models have been adopted to implement Network Intrusion Detection Systems (NIDSs) It is important to select the right set of data features, which maximise the detection accuracy as well as computational efficiency. This paper provides an extensive analysis of the optimal feature sets in terms of the importance and predictive power of network attacks.
  arXiv  Detail & Related papers  (2021-08-29T02:19:37Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach [40.992167455141946]
  This paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. We first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-textitk selection to
  arXiv  Detail & Related papers  (2020-07-19T16:47:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.