skin mounted medical devices

Shock therapy for medical device malware

Israel has over 700 medical device vendors.  Sometimes it seems like half of them are attaching to the cloud and the other are developing mobile apps for all kinds of crazy, innovative applications like Healthy.io ( Visual Input Turned Into Powerful Medical Insight – translation: an app that lets you do urine analysis using your smart phone).

But – let’s not forget that many Medical devices  such as bedside monitors, MRI, nuclear medicine and  catheterization devices all reside on today’s hospital enterprise network.

An enterprise hospital network is a dangerous place.

Medical devices based on Microsoft Windows  can be extremely vulnerable to attack from hackers and malware who penetrate the hospital network and exploit typical vulnerabilities such as default passwords.

More importantly – medical devices that are attached to a hospital network are a significant threat to the hospital network itself since they may propagate malware back into the network.

While a thorough software security assessment of the medical device and appropriate hardening of the operating system and user-space code is the best way to secure a medical device in a hostile hospital network – this is not usually an option for the hospital once the medical device is installed.

Taking a page out of side-channel attacks and using the technique to detect malware, University of Michigan researchers have developed WattsUpDoc, a system designed to detect malware on medical devices by noting small changes in their power consumption.

The researchers say the technology could give hospitals a quick way to identify medical devices with significant vulnerabilities.

The researchers tested WattsUpDoc on an industrial-control workstation and on a compounder, which is used to mix drugs.

The malware detector first learned the devices’ normal power-consumption patterns. Then it tested machines that had been intentionally infected with malware. The system was able to detect abnormal activity more than 94 percent of the time when it had been trained to recognize the malware, and up to 91 percent of the time with previously unknown malware. The researchers say the technology could alert hospital IT administrators that something is wrong, even if the exact virus is never identified.

For the full article see WattsUpDoc

 

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