Background Modern healthcare devices can be connected to computer networks and many western healthcare institutions run those devices in networks. At the same time, cyber attacks are on the rise and there is evidence that cybercriminals do not spare critical infrastructure such as major hospitals, even if they endanger patients. Intuitively, the more and closer connected healthcare devices are to public networks, the higher the risk of getting attacked. Methods To asses the current connectivity status of healthcare devices, we surveyed the field of German hospitals and especially University Medical Center UMCs. Results The results show a strong correlation between the networking degree and the number of medical devices. The average number of medical devices is 25.150, with a median of networked medical devices of 3.600. Actual key users of networked medical devices are the departments Radiology, Intensive Care, Radio-Oncology RO, Nuclear Medicine NUC, and Anaesthesiology in the group of UMCs. In the next five years, the usage of networked medical devices will increase significantly in the departments of Surgery, Intensive Care, and Radiology. We detected a strong correlation between the degree of connectivity and the likelihood of being attacked.The survey answers regarding the cyber security status reveal a lack of security basics in some of the inquired hospitals. We did discover successful attacks in hospitals with separated or subsidiary departments. A fusion of competencies on an organizational level facilitates the right behavior here. Most hospitals rated themselves predominantly positively in the self-assessment but also stated the usefulness of IT security insurance. Conclusions Concluding our results, hospitals are already facing the consequences of omitted measures within their growing pool of medical devices. Continuously relying on historically grown structures without adaption and trusting manufactures to solve vectors is a critical behavior that could seriously endanger patients.

Technical and organizational steps are necessary to mitigate cyber threats and reduce risks. Human behavior is the last line of defense for many hospitals and is considered as equally important as technical security. Medical staff must be properly trained to perform such procedures. This paper presents the first qualitative, interdisciplinary research on how members of an intermediate care unit react to a cyberattack against their patient monitoring equipment. We conducted a simulation in a hospital training environment with 20 intensive care nurses. By the end of the experiment, 12 of the 20 participants realized the monitors’ incorrect behavior. We present a qualitative behavior analysis of high performing participants (HPP) and low performing participants (LPP). The HPP showed fewer signs of stress, were easier on their colleagues, and used analog systems more often than the LPP. With 40% of our participants not recognizing the attack, we see room for improvements through the use of proper tools and provision of adequate training to prepare staff for potential attacks in the future.

Vulnerabilities in private networks are difficult to detect for attackers outside of the network. While there are known methods for port scanning internal hosts that work by luring unwitting internal users to an external web page that hosts malicious JavaScript code, no such method for detailed and precise service identification is known. The reason is that the Same Origin Policy (SOP) prevents access to HTTP responses of other origins by default. We perform a structured analysis of loopholes in the SOP that can be used to identify web applications across network boundaries. For this, we analyze HTML5, CSS, and JavaScript features of standardcompliant web browsers that may leak sensitive information about cross-origin content. The results reveal several novel techniques, including leaking JavaScript function names or styles of cross-origin requests that are available in all common browsers. We implement and test these techniques in a tool called COR-SICA. It can successfully identify 31 of 42 (74%) of web services running on different IoT devices as well as the version numbers of the four most widely used content management systems WordPress, Drupal, Joomla, and TYPO3. CORSICA can also determine the patch level on average down to three versions (WordPress), six versions (Drupal), two versions (Joomla), and four versions (TYPO3) with only ten requests on average. Furthermore, CORSICA is able to identify 48 WordPress plugins containing 65 vulnerabilities. Finally, we analyze mitigation strategies and show that the proposed but not yet implemented strategies Cross-Origin Resource Policy (CORP) and Sec-Metadata would prevent our identification techniques.

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