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Cybersecurity in neural interfaces: Survey and future trends.

Xinyu Jiang1, Jiahao Fan2, Ziyue Zhu3

  • 1School of Information Science and Technology, Fudan University, Shanghai, China.

Computers in Biology and Medicine
|October 26, 2023
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Summary

This survey explores cybersecurity risks in neural interfaces, covering brain-computer and peripheral systems. It identifies data, permission, and model-level threats and proposes solutions for secure neural data applications.

Keywords:
Adversarial attacks and defensesCybersecurityNeural interfacesPrivacy preservationSystem security

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Area of Science:

  • Neuroscience and Cybersecurity
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Neural interfaces are advancing rapidly, integrating sensing, computing, neuromodulation, and AI.
  • These interfaces support neurorehabilitation and human-machine interaction but face emerging cybersecurity challenges.
  • Existing research has largely overlooked the security implications of neural interface technologies.

Purpose of the Study:

  • To systematically investigate cybersecurity risks associated with neural interfaces.
  • To propose potential solutions to mitigate identified security threats.
  • To provide a comprehensive overview of neural interface security across different systems and levels.

Main Methods:

  • Systematic review of cybersecurity risks in neural interfaces.
  • Analysis of risks at data, permission, and model levels.
  • Consideration of both central nervous system (brain-computer interfaces) and peripheral nervous system interfaces.
  • Inclusion of diverse neural modalities like electroencephalography and electromyography.

Main Results:

  • Identified data-level risks: privacy leakage through neural database attacks.
  • Identified permission-level risks: secure use of real-time neural signals for continuous user verification.
  • Identified model-level risks: adversarial attacks on decoding and feedback models, with proposed defenses.

Conclusions:

  • This is the first study to comprehensively survey cybersecurity risks and solutions for both central and peripheral neural interfaces.
  • The multi-level analysis (data, permission, model) offers a complete picture of neural interface security.
  • Addressing these cybersecurity concerns is crucial for the safe and effective deployment of neural interface technologies.