Internet of Medical Things (IoMT) devices are revolutionizing the healthcare business.  They are delivering on the promises of improved data access, convenience, operational efficiencies and automation for healthcare providers (HCP). Plus, additional drivers like patient engagement and safety, accuracy of vitals, and mobility within the institution promise continuing, potentially explosive growth in smaller, connected medical instruments.

That’s the good news. The bad news is that healthcare IT groups are struggling with the proliferation of IoMT devices, which currently average three to six monitoring devices per admitted patient. In this new world of digital medicine, the big challenge is in ensuring that both data privacy and patient safety remain top priorities.

Security Information Event Management (SIEM) systems, like IBM’s QRadar, are the major tool HCPs are using to successfully deal with this explosive growth in connected devices and data. Gartner’s Magic Quadrant for Security Information and Event Management 2018 report (Registration Required) defines the SIEM market as "the customer’s need to analyze event data in real time for early detection of targeted attacks and data breaches, and to collect, store, investigate and report on log data for incident response, forensics and regulatory compliance.” SIEM promises to make security events more digestible and actionable, especially when cognitive computing is brought to bear on the large volume of aggregated data that floods in from the networked devices.

But even the most sophisticated SIEM systems struggle in today’s hyper-connected medical institutions. Here are three examples:

Example 1: Integrating instruments based on small, embedded processors and real-time operating systems
It can be a challenge to extend the reach of the SIEM into devices that have very constrained resources and may not be able to support standard logging. Supporting the wide diversity of device platforms and operating systems within the healthcare environment is equally daunting.

Example 2: Allowing for untrusted devices
Mobile devices like smartphones and tablets that host medical applications must be considered as hostile environments.  They are often unmanaged (or even user-owned BYOD) that can leave the premises and allow a hacker the luxuries of time and access in order to craft an attack. This is also a concern for the emerging category of patient wearables.

Example 3: Scaling to thousands of devices
The problem of scaling boils down to a matter of trust in the data. If a motivated hacker compromises the device, they can manipulate the data being sent or block it entirely. SIEM systems use a combination of forensics, behavior and data from multiple sources to combat this, but as the numbers of connected devices deployed in medical institutions grow exponentially, the shear volume of log events that must be processed to discern critical security events becomes untenable. This is especially a problem for SIEM systems that may use artificial intelligence/cognitive processing to detect bad behavior of a device to use as a cybersecurity event alert. The increasing numbers of alerts put severe pressure on the ability of the system to produce timely security events as well.

One solution to these problems is via a "trusted" security telemetry agent and associated services. By using software protection techniques, it is possible to conceive of a secured telemetry agent, that could produce reliable and robust critical security events in near real-time, without any complicated forensics and additional processing required.

The agent should be written in protected source code for easy portability between platforms and operating systems with a small footprint in code and memory size for instruments with constrained resources. The agent would leverage software protection features for anti-reverse engineering, and anti-tampering, as well as utilizing integrity verification to establish a software-based root-of-trust.

End-to-end protected communications to a dedicated server for aggregation, filtering and reporting rounds out the high-level feature set.

With the features above in place, critical security events can be logged, and the data cannot be compromised by attackers in the generation phase, at rest, or in transit. Such a self-protecting, security-in-depth implementation means you can extend trust to untrusted devices and the pre-qualified security events would help the SIEM extend its reach and scale to thousands of IoMT devices easily!

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About the Author

Mark Hearn, Director of IoT Security, Irdeto

Mark Hearn is the director of IoT Security at Irdeto. He is responsible for leading business development strategies to secure organizations IoT applications and connected devices. Mark has been with Irdeto since 2003, through Irdeto’s acquisition of Cloakware. He is a seasoned product management executive with 20 years of bringing technology and business requirements together to solve market problems, particularly within the media entertainment and security markets. In addition to being a product leader in the private sector, Mark has also provided business analysis security consulting into the Canadian government and has spoken at security conferences. Mark holds a Bachelor of Computer Science from Acadia University in Nova Scotia, Canada and has received certifications in product management, technical marketing and strategic marketing.