The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain’s unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.

The research area of honeypots is gaining new momentum, driven by advancements in large language models (LLMs). The chat-based applications of generative pretrained transformer (GPT) models seem ideal for the use as honeypot backends, especially in request-response protocols like Secure Shell (SSH). By leveraging LLMs, many challenges associated with traditional honeypots – such as high development costs, ease of exposure, and breakout risks – appear to be solved. While early studies have primarily focused on the potential of these models, our research investigates the current limitations of GPT-3.5 by analyzing three datasets of varying complexity. We conducted an expert annotation of over 1,400 request-response pairs, encompassing 230 different base commands. Our findings reveal that while GPT-3.5 struggles to maintain context, incorporating session context into response generation improves the quality of SSH responses. Additionally, we explored whether distinguishing between convincing and non-convincing responses is a metrics issue. We propose a paraphrase-mining approach to address this challenge, which achieved a macro F1 score of 77.85% using cosine distance in our evaluation. This method has the potential to reduce annotation efforts, converge LLM-based honeypot performance evaluation, and facilitate comparisons between new and previous approaches in future research.

The growing number of cyberattacks targeting the healthcare sector increasingly threatens network-enabled medical devices that are vital for life-sustaining patient care. Security researchers and healthcare IT managers are pursuing effective methods to assess the IT security landscape of medical devices and aim to develop a comprehensive understanding of their IT security status. Recent studies successfully uncover structural deficiencies in medical device security, but the limitations of their data sources, particularly in evaluating features like logging capabilities and utilized third-party libraries, restrict their scope. In this study, we present the first systematic analysis of Manufacturer Disclosure Statement for Medical Device Security (MDS2) documents to evaluate their use in creating holistic statements regarding the IT security posture of medical devices. We examined a total of 147 MDS2 documents encompassing devices from 105 different classes. Our findings indicate that MDS2 documents, especially those from the second version (2013) onwards, are suitable for this purpose. We also discuss shortcomings of the latest MDS2 version in meeting current IT security requirements. Based on the gaps identified, we developed several recommendations for the further development of MDS2 documents to enhance their effectiveness and applicability across the global healthcare sector. In the future, these documents could be used not only for comprehensive landscape analyses but be further leveraged for organization-specific reports that provide healthcare managers with direct insights into the IT security status within their institutions.

Introduction: The growing number of connected medical devices in hospitals poses serious operational technology (OT) security challenges. Effective countermeasures require a structured analysis of the communication interfaces and security configurations of individual devices. State of the art: Although Manufacturer Disclosure Statements for Medical Device Security (MDS2, Version 2019) offer relevant information, they are rarely integrated into cybersecurity workflows. Existing studies are limited in scope and lack scalable methodologies for systematic evaluation. Concept: This study analyzed 209 MDS2 documents and 161 security white papers to extract structured information on ports, protocols, and protective measures. Over 52,000 question–answer pairs were converted into a machine-readable format using customized parsing and validation routines. The aim was to establish whether this dataset could inform risk assessments and future applications involving Large Language Models (LLMs). Implementation: The analysis revealed 367 distinct ports, including common protocols such as HTTPS (443), DICOM (104), and RDP (3389), as well as vendor-specific proprietary ports. Approximately 40% of the devices used over 20 ports, indicating a broad attack surface. OCR errors and inconsistent formatting required manual corrections. A consolidated dataset was developed to support clustering, comparison across vendors and versions, and preparation for downstream LLM use, particularly via structured SBOM and configuration data. Lessons learned: Although no model training was conducted, the structured dataset can support AI-based OT security workflows. The findings highlight the critical need for up-to-date, machine-readable manufacturer data in standardized formats and schemas. Such information could greatly enhance the automation, comparability, and scalability of hospital cybersecurity measures.

This paper investigates cybersecurity threats in medical IT (Information Technology) systems exposed to the Internet. To that end, we develop a methodology and build a data processing pipeline that allows to gather data from different OSINT (Open Source Intelligence) sources, and processes it to obtain relevant cybersecurity metrics. To validate its operation and usefulness, we apply it to two countries, Germany and Spain, allowing to study the main threats that affect medical IT systems in these countries. Our initial findings reveal that 20% of German hosts and 15% of Spanish hosts tagged as medical devices have at least one CVE (Common Vulnerabilities and Exposures) with a CVSS (Common Vulnerability Scoring System) graded as critical (i.e., value 8 or greater). Moreover, we found that 74% of CVEs found in German hosts are dated from earlier than 2020, whereas for Spanish hosts the percentage is 60%. This indicates that medical IT systems exposed to the Internet are seldom updated, which further increases their exposure to cyberthreats. Based on these initial findings, we finish the paper providing some insights on how to improve cybersecurity of these systems.