Researchers from US-based non-profit organisation American Associates Ben-Gurion University of the Negev have urged medical imaging device (MID) manufacturers and healthcare providers to be more careful in protecting the imaging equipment from cyber threats.
In a paper titled: ‘Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices’, the researchers outlined the relative ease with which unpatched medical devices such as CT and MRI machines that lack updated security can be exploited.
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Computers that control the CT devices are prone to cyberattack, which could cause severe damage to patients.
According to the researchers, the attacks on MIDs will rise as the majority of their mechanics and software are connected to outdated Microsoft computers.
Malware Lab researcher Tom Mahler said: “The MID development process, from concept to market, takes three to seven years.
“Cyber threats can change significantly over that period, which leaves medical imaging devices highly vulnerable.”
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By GlobalDataThe research, which was led by the organisation’s Malware Lab head Dr Nir Nissim, is part of the Cyber-Med project that is designed to create security mechanisms for the complete medical device ecosystem.
It was conducted in alliance with Israel’s Clalit Health Services and involved a comprehensive risk analysis survey on vulnerabilities and potential attacks on MIDs, medical and imaging information systems, medical protocols, and standards.
The survey revealed that CT devices are at a comparatively greater risk of attack as they are important for acute care imaging.
Simulated cyberattacks conducted during the study showed that interruption to scan configuration files, mechanical MID motor, and image results could occur.
These attacks could also result in ransomware that affects numerous hospitals and patients.
Malware Lab researchers are currently working towards developing new techniques based on machine learning methods to secure CT devices.
Depending on a clean CT machine, the machine learning algorithm will create an anomaly detection model, which will be able to identify any change in behaviour and operational parameters of the device.
