Eskenzi PR ad banner Eskenzi PR ad banner
  • About Us
Tuesday, 31 January, 2023
IT Security Guru
Eskenzi PR banner
  • Home
  • Features
  • Insight
  • Events
    • Most Inspiring Women in Cyber 2022
  • Topics
    • Cloud Security
    • Cyber Crime
    • Cyber Warfare
    • Data Protection
    • DDoS
    • Hacking
    • Malware, Phishing and Ransomware
    • Mobile Security
    • Network Security
    • Regulation
    • Skills Gap
    • The Internet of Things
    • Threat Detection
    • AI and Machine Learning
    • Industrial Internet of Things
  • Multimedia
  • Product Reviews
  • About Us
No Result
View All Result
  • Home
  • Features
  • Insight
  • Events
    • Most Inspiring Women in Cyber 2022
  • Topics
    • Cloud Security
    • Cyber Crime
    • Cyber Warfare
    • Data Protection
    • DDoS
    • Hacking
    • Malware, Phishing and Ransomware
    • Mobile Security
    • Network Security
    • Regulation
    • Skills Gap
    • The Internet of Things
    • Threat Detection
    • AI and Machine Learning
    • Industrial Internet of Things
  • Multimedia
  • Product Reviews
  • About Us
No Result
View All Result
IT Security Guru
No Result
View All Result

New technique designed to prevent medical imaging cyberthreats

Israeli researchers prevent abnormal or anomalous instructions posing potentially harmful threats to patients

by Tony Morbin
August 27, 2020
in Cyber Bites, Security News
New technique designed to prevent medical imaging cyberthreats
Share on FacebookShare on Twitter

A new artificial intelligence technique has been created to protect medical devices from malicious operating instructions in a cyberattack as well as other human and system errors.

Tom Mahler, a researcher at Ben-Gurion University of the Negev, Israel, presented his research, “A Dual-Layer Architecture for the Protection of Medical Devices from Anomalous Instructions” on August 26 at the 2020 International Conference on Artificial Intelligence in Medicine (AIME 2020).

Complex medical devices such as CT (computed tomography), MRI (magnetic resonance imaging) and ultrasound machines are controlled by instructions sent from a host PC.  Abnormal or anomalous instructions introduce many potentially harmful threats to patients, such as radiation overexposure, manipulation of device components or functional manipulation of medical images. Threats can occur due to cyberattacks, human errors such as a technician’s configuration mistake or host PC software bugs.

Mahler has developed a technique using artificial intelligence that analyses the instructions sent from the PC to the physical components using a new architecture for the detection of anomalous instructions.

“We developed a dual-layer architecture for the protection of medical devices from anomalous instructions,” Mahler says. “The architecture focuses on detecting two types of anomalous instructions: (1) context-free (CF) anomalous instructions which are unlikely values or instructions such as giving 100x more radiation than typical, and (2) context-sensitive (CS) anomalous instructions, which are normal values or combinations of values, of instruction parameters, but are considered anomalous relative to a particular context, such as mismatching the intended scan type, or mismatching the patient’s age, weight, or potential diagnosis.

“For example, a normal instruction intended for an adult might be dangerous [anomalous] if applied to an infant. Such instructions may be misclassified when using only the first, CF, layer; however, by adding the second, CS, layer, they can now be detected.”

The research team evaluated the new architecture in the computed tomography (CT) domain, using 8,277 recorded CT instructions and evaluated the CF layer using 14 different unsupervised anomaly detection algorithms. Then they evaluated the CS layer for four different types of clinical objective contexts, using five supervised classification algorithms for each context.

Adding the second CS layer to the architecture improved the overall anomaly detection performance from an F1 score of 71.6 percent, using only the CF layer, to between 82 percent and 99 percent, depending on the clinical objective or the body part. The CS layer also enables the detection of CS anomalies, using the semantics of the device’s procedure, an anomaly type that cannot be detected using only the CF layer.

Mahler is a Ph.D. candidate under the supervision of BGU Profs. Yuval Elovici and Prof. Yuval Shahar in the BGU Department of Software and Information Systems Engineering (SISE).  In addition to Shahar and Elovici Dr Erez Shalom, also a senior researcher in the Medical Informatics Research Center, participated in the study.

FacebookTweetLinkedIn
Share6TweetShare
Previous Post

Clar Rosso appointed CEO (ISC)²

Next Post

The UK’s exposure & resilience to cyberattacks

Recent News

JD Sports admits data breach

JD Sports admits data breach

January 30, 2023
Acronis seals cyber protection partnership with Fulham FC

Acronis seals cyber protection partnership with Fulham FC

January 30, 2023
Data Privacy Day: Securing your data with a password manager

Data Privacy Day: Securing your data with a password manager

January 27, 2023
#MIWIC2022: Carole Embling, Metro Bank

#MIWIC2022: Carole Embling, Metro Bank

January 26, 2023

The IT Security Guru offers a daily news digest of all the best breaking IT security news stories first thing in the morning! Rather than you having to trawl through all the news feeds to find out what’s cooking, you can quickly get everything you need from this site!

Our Address: 10 London Mews, London, W2 1HY

Follow Us

© 2015 - 2019 IT Security Guru - Website Managed by Calm Logic

  • About Us
No Result
View All Result
  • Home
  • Features
  • Insight
  • Events
    • Most Inspiring Women in Cyber 2022
  • Topics
    • Cloud Security
    • Cyber Crime
    • Cyber Warfare
    • Data Protection
    • DDoS
    • Hacking
    • Malware, Phishing and Ransomware
    • Mobile Security
    • Network Security
    • Regulation
    • Skills Gap
    • The Internet of Things
    • Threat Detection
    • AI and Machine Learning
    • Industrial Internet of Things
  • Multimedia
  • Product Reviews
  • About Us

© 2015 - 2019 IT Security Guru - Website Managed by Calm Logic

This site uses functional cookies and external scripts to improve your experience.

Privacy settings

Privacy Settings / PENDING

This site uses functional cookies and external scripts to improve your experience. Which cookies and scripts are used and how they impact your visit is specified on the left. You may change your settings at any time. Your choices will not impact your visit.

NOTE: These settings will only apply to the browser and device you are currently using.

GDPR Compliance

Powered by Cookie Information