A Novel Zero-Trust Machine Learning Green Architecture for Healthcare IoT Cybersecurity: Review, Analysis, and Implementation

• URL: http://arxiv.org/abs/2401.07368v1
• Date: Sun, 14 Jan 2024 21:01:21 GMT
• Title: A Novel Zero-Trust Machine Learning Green Architecture for Healthcare IoT Cybersecurity: Review, Analysis, and Implementation
• Abstract summary: The integration of Internet of Things (IoT) devices in healthcare applications has revolutionized patient care, monitoring, and data management. However, the rapid involvement of these devices brings information security concerns that pose critical threats to patient privacy and the integrity of healthcare data. This paper introduces a novel machine learning (ML) based architecture explicitly designed to address and mitigate security vulnerabilities in IoT devices within healthcare applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Internet of Things (IoT) devices in healthcare applications has revolutionized patient care, monitoring, and data management. The Global IoT in Healthcare Market value is $252.2 Billion in 2023. However, the rapid involvement of these devices brings information security concerns that pose critical threats to patient privacy and the integrity of healthcare data. This paper introduces a novel machine learning (ML) based architecture explicitly designed to address and mitigate security vulnerabilities in IoT devices within healthcare applications. By leveraging advanced convolution ML architecture, the proposed architecture aims to proactively monitor and detect potential threats, ensuring the confidentiality and integrity of sensitive healthcare information while minimizing the cost and increasing the portability specialized for healthcare and emergency environments. The experimental results underscore the accuracy of up to 93.6% for predicting various attacks based on the results demonstrate a zero-day detection accuracy simulated using the CICIoT2023 dataset and reduces the cost by a factor of x10. The significance of our approach is in fortifying the security posture of IoT devices and maintaining a robust implementation of trustful healthcare systems.

Related papers

• On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT [13.996271196616364]
  This paper proposes a novel on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) to identify various phases of cyberattacks in IoMT environments.<n>Extensive evaluations on the MedSec-25 dataset, encompassing various phases of realistic cyberattacks, show that the proposed model outperforms ML models and state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-15T23:44:40Z)
• Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis [39.89241412792336]
  We analyzed eight attack scenarios in four categories: architectural attacks on convolutional neural networks, large language models, and reinforcement learning agents.<n>Our findings indicate that attackers with access to only 100-500 samples can compromise healthcare AI regardless of dataset size.<n>We recommend multilayer defenses including required adversarial testing, ensemble-based detection, privacy-preserving security mechanisms, and international coordination on AI security standards.
  arXiv  Detail & Related papers  (2025-11-14T07:16:16Z)
• Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things [0.5097809301149341]
  The rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management.<n>These technological innovations raise critical security concerns, particularly in safeguarding medical data against potential cyber threats.<n>We propose a comprehensive method encompassing three distinct phases to address the imperative need for enhanced security in IoT-based healthcare systems.
  arXiv  Detail & Related papers  (2025-10-21T19:09:46Z)
• Privacy-Preserving Healthcare Data in IoT: A Synergistic Approach with Deep Learning and Blockchain [0.5097809301149341]
  The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management.<n>Traditional security measures are often insufficient to address the unique challenges posed by IoT environments.<n>We propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems.
  arXiv  Detail & Related papers  (2025-10-21T12:21:49Z)
• Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models [87.66870367661342]
  Large language models (LLMs) are used in AI applications in healthcare.<n>Red-teaming framework that continuously stress-test LLMs can reveal significant weaknesses in four safety-critical domains.<n>A suite of adversarial agents is applied to autonomously mutate test cases, identify/evolve unsafe-triggering strategies, and evaluate responses.<n>Our framework delivers an evolvable, scalable, and reliable safeguard for the next generation of medical AI.
  arXiv  Detail & Related papers  (2025-07-30T08:44:22Z)
• Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation [69.63626052852153]
  We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems.<n>We also introduce a dataset of 100 indirect harm scenarios, testing models' ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts.
  arXiv  Detail & Related papers  (2025-06-26T02:28:58Z)
• Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices [6.197430230611422]
  We analyze publicly available data on device recalls and adverse events, and known vulnerabilities, to understand the threat landscape of AI/ML-enabled medical devices.<n>Our work aims to empower manufacturers to embed cybersecurity as a core design principle in AI/ML-enabled medical devices.
  arXiv  Detail & Related papers  (2025-06-18T00:05:48Z)
• Trustworthy Medical Question Answering: An Evaluation-Centric Survey [40.76054471378203]
  We systematically examine six key dimensions of trustworthiness in medical question-answering systems.<n>We analyze evaluation-guided techniques that drive model improvements.<n>We propose future research directions to advance the safe, reliable, and transparent deployment of LLM-powered medical QA.
  arXiv  Detail & Related papers  (2025-06-04T07:48:10Z)
• Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway [45.70482328441101]
  This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic by analyzing network behavior at the edge. We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic. This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.
  arXiv  Detail & Related papers  (2025-04-22T09:40:05Z)
• Safeguarding Smart Inhaler Devices and Patient Privacy in Respiratory Health Monitoring [0.0]
  An estimated 75 billion devices will be on the market in 2025. Chronic respiratory disease is likely to benefit from their inclusion, with 545 million people worldwide recorded to suffer from patients using these devices to track their dosage. While IoT medical devices offer numerous benefits, they also have security vulnerabilities that can expose patient data to cyberattacks.
  arXiv  Detail & Related papers  (2025-03-31T18:16:06Z)
• CryptoDNA: A Machine Learning Paradigm for DDoS Detection in Healthcare IoT, Inspired by crypto jacking prevention Models [0.0]
  The rapid integration of the Internet of Things (IoT) and Internet of Medical (IoM) devices in the healthcare industry has markedly improved patient care and hospital operations. DDoS attacks present significant dangers, jeopardizing operational stability and patient safety. This study introduces CryptoDNA, an innovative machine learning detection framework influenced by cryptojacking detection methods.
  arXiv  Detail & Related papers  (2025-01-30T18:22:16Z)
• A Review on the Security Vulnerabilities of the IoMT against Malware Attacks and DDoS [0.0]
  The Internet of Medical Things (IoMT) has transformed the healthcare industry by connecting medical devices in monitoring treatment outcomes of patients. This literature review examines the vulnerabilities of IoMT devices, focusing on critical threats and exploring mitigation strategies.
  arXiv  Detail & Related papers  (2025-01-13T21:29:06Z)
• A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers [37.285731240749904]
  This paper introduces a novel Federated Learning (FL) platform designed to support the configuration, monitoring, and management of FL processes. Considering the production sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture. The platform has been successfully tested in various operational environments using a publicly available dataset.
  arXiv  Detail & Related papers  (2024-10-02T09:24:05Z)
• Adversarial Attacks on Large Language Models in Medicine [34.17895005922139]
  The integration of Large Language Models into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care.<n>The susceptibility of LLMs to adversarial attacks poses a significant threat, potentially leading to harmful outcomes in delicate medical contexts.<n>This study investigates the vulnerability of LLMs to two types of adversarial attacks in three medical tasks.
  arXiv  Detail & Related papers  (2024-06-18T04:24:30Z)
• SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks [37.89720165358964]
  SpikingJET is a novel fault injector designed specifically for fully connected and convolutional Spiking Neural Networks (SNNs) Our work underscores the critical need to evaluate the resilience of SNNs to hardware faults, considering their growing prominence in real-world applications.
  arXiv  Detail & Related papers  (2024-03-30T14:51:01Z)
• Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems [1.8434042562191815]
  The Internet of Medical Things (IoMT) transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive prevention. Its benefits are countered by significant security challenges that endanger the lives of its users due to the sensitivity and value of the processed data. A new framework for Intrusion Detection Systems (IDS) is introduced, leveraging Artificial Neural Networks (ANN) for intrusion detection while utilizing Federated Learning (FL) for privacy preservation.
  arXiv  Detail & Related papers  (2024-03-14T11:57:26Z)
• Highlighting the Safety Concerns of Deploying LLMs/VLMs in Robotics [54.57914943017522]
  We highlight the critical issues of robustness and safety associated with integrating large language models (LLMs) and vision-language models (VLMs) into robotics applications.
  arXiv  Detail & Related papers  (2024-02-15T22:01:45Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation [4.499833362998488]
  This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application.
  arXiv  Detail & Related papers  (2024-01-17T10:55:26Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique [0.44040106718326594]
  Security and privacy are key concerns on the Internet of Medical Things (IoMT) industry. By identifying concerns early, a smart healthcare system can help avoid long-term damage. This study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS)
  arXiv  Detail & Related papers  (2022-09-16T23:25:42Z)
• Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things [2.223733768286313]
  Internet of Medical Things (IoMT) represents an application of the Internet of Things. The sensitive and private nature of this data may represent a prime interest for attackers. The use of traditional security methods on equipment that is limited in terms of storage and computing capacity is ineffective.
  arXiv  Detail & Related papers  (2022-02-19T18:40:55Z)
• Edge Intelligence for Empowering IoT-based Healthcare Systems [42.909808437026136]
  This article highlights the benefits of edge intelligent technology, along with AI in smart healthcare systems. A novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems.
  arXiv  Detail & Related papers  (2021-03-22T19:35:06Z)
• A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems [2.519906683279153]
  We propose SHChecker, a novel threat analysis framework that integrates machine learning and formal analysis capabilities. We implement SHChecker on a synthetic and a real dataset, which affirms that our framework can reveal potential attack vectors in an IoMT system.
  arXiv  Detail & Related papers  (2021-03-05T04:51:02Z)
• Automated Identification of Vulnerable Devices in Networks using Traffic Data and Deep Learning [30.536369182792516]
  Device-type identification combined with data from vulnerability databases can pinpoint vulnerable IoT devices in a network. We present and evaluate two deep learning approaches to the reliable IoT device-type identification.
  arXiv  Detail & Related papers  (2021-02-16T14:49:34Z)
• I-Health: Leveraging Edge Computing and Blockchain for Epidemic Management [36.55809341110476]
  Epidemic situations demand intensive data collection and management from different locations/entities within a strict time constraint. This paper proposes an Intelligent-Health (I-Health) system that aims to aggregate diverse e-health entities in a unique national healthcare system. In particular, we design an automated patients monitoring scheme, at the edge, which enables the prompt discovery, remote monitoring, and fast emergency response.
  arXiv  Detail & Related papers  (2020-12-18T23:41:00Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)

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.