Threats and Security Strategies for IoMT Infusion Pumps
- URL: http://arxiv.org/abs/2509.14604v1
- Date: Thu, 18 Sep 2025 04:29:07 GMT
- Title: Threats and Security Strategies for IoMT Infusion Pumps
- Authors: Ramazan Yener, Muhammad Hassan, Masooda Bashir,
- Abstract summary: This study focuses on the cybersecurity vulnerabilities of IoMT infusion pumps, which are critical devices in modern healthcare.<n>Our findings indicate that infusion pumps face vulnerabilities such as device-level flaws, authentication and access control issues, network and communication weaknesses, data security and privacy risks, and operational or organizational challenges that can expose them to lateral attacks within healthcare networks.
- Score: 0.8572622875928976
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The integration of the Internet of Medical Things (IoMT) into healthcare systems has transformed patient care by enabling real-time monitoring, enhanced diagnostics, and enhanced operational efficiency. However, this increased connectivity has also expanded the attack surface for cybercriminals, raising significant cybersecurity and privacy concerns. This study focuses on the cybersecurity vulnerabilities of IoMT infusion pumps, which are critical devices in modern healthcare. Through a targeted literature review of the past five years, we analyzed seven current studies from a pool of 132 papers to identify security vulnerabilities. Our findings indicate that infusion pumps face vulnerabilities such as device-level flaws, authentication and access control issues, network and communication weaknesses, data security and privacy risks, and operational or organizational challenges that can expose them to lateral attacks within healthcare networks. Our analysis synthesizes findings from seven recent studies to clarify how and why infusion pumps remain vulnerable in each of these areas. By categorizing the security gaps, we highlight critical risk patterns and their implications. This work underscores the scope of the issue and provides a structured understanding that is valuable for healthcare IT professionals and device manufacturers. Ultimately, the findings can inform the development of targeted, proactive security strategies to better safeguard infusion pumps and protect patient well-being.
Related papers
- An Empirical Study on the Security Vulnerabilities of GPTs [48.12756684275687]
GPTs are one kind of customized AI agents based on OpenAI's large language models.<n>We present an empirical study on the security vulnerabilities of GPTs.
arXiv Detail & Related papers (2025-11-28T13:30:25Z) - Explainable and Resilient ML-Based Physical-Layer Attack Detectors [46.30085297768888]
We analyze the inner workings of various classifiers trained to alert about physical layer intrusions.<n>We evaluate the detectors' resilience to malicious parameter noising.<n>This work serves as a design guideline for developing fast and robust detectors trained on available network monitoring data.
arXiv Detail & Related papers (2025-09-30T17:05:33Z) - 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) - Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks [1.9950682531209158]
We show how explainable AI techniques like SHAP, LIME, and DiCE can uncover vulnerabilities, strengthen defenses, and improve trust and transparency in 6G enabled healthcare.
arXiv Detail & Related papers (2025-05-20T17:46:09Z) - MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN [42.74889568823579]
We identify a new time-exciting threat model against in-vehicle network (IVN)<n>These attacks inject malicious messages that exhibit a time-exciting effect, gradually manipulating network traffic to disrupt vehicle operations and compromise safety-critical functions.<n>To detect time-exciting threat, we introduce MDHP-Net, leveraging Multi-Dimentional Hawkes Process (MDHP) and temporal and message-wise feature extracting structures.
arXiv Detail & Related papers (2025-04-16T08:41:24Z) - Cybersecurity and Frequent Cyber Attacks on IoT Devices in Healthcare: Issues and Solutions [0.0]
Internet of Things (IoT) devices in healthcare have revolutionized patient care, offering improved monitoring, diagnostics, and treatment.<n>However, the proliferation of these devices has also introduced significant cybersecurity challenges.<n>This paper reviews the current landscape of cybersecurity threats targeting IoT devices in healthcare, discusses the underlying issues contributing to these vulnerabilities, and explores potential solutions.
arXiv Detail & Related papers (2025-01-20T03:29:07Z) - 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.<n>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) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN [42.74889568823579]
We identify a new time-exciting threat model against in-vehicle network (IVN)<n>These attacks inject malicious messages that exhibit a time-exciting effect, gradually manipulating network traffic to disrupt vehicle operations and compromise safety-critical functions.<n>To detect time-exciting threat, we introduce MDHP-Net, leveraging Multi-Dimentional Hawkes Process (MDHP) and temporal and message-wise feature extracting structures.
arXiv Detail & Related papers (2024-11-15T15:05:01Z) - Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security [1.2369895513397127]
Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated.
To efficiently secure IoT devices, real-time detection of intrusion systems is critical.
This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
arXiv Detail & Related papers (2024-10-01T19:24:34Z) - Physical Adversarial Attacks for Surveillance: A Survey [40.81031907691243]
This paper reviews recent attempts and findings in learning and designing physical adversarial attacks for surveillance applications.
In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks.
The insights in this paper present an important step in building resilience within surveillance systems to physical adversarial attacks.
arXiv Detail & Related papers (2023-05-01T20:19:59Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
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.