Security of Medical Cyber-physical Systems: An Empirical Study on Imaging Devices (1904.00224v2)

Abstract: Recent years have witnessed a boom of connected medical devices, which brings security issues in the meantime. Medical imaging devices, an essential part of medical cyber-physical systems, play a vital role in modern hospitals and are often life-critical. However, security and privacy issues in these medical cyber-physical systems are sometimes ignored. In this paper, we perform an empirical study on imaging devices to analyse the security of medical cyber-physical systems. To be precise, we design a threat model and propose prospective attack techniques for medical imaging devices. To tackle potential cyber threats, we introduce protection mechanisms, evaluate the effectiveness and efficiency of protection mechanisms as well as its interplay with attack techniques. To scoring security, we design a hierarchical system that provides actionable suggestions for imaging devices in different scenarios. We investigate 15 devices from 9 manufacturers to demonstrate empirical comprehension and real-world security issues.

• The paper introduces a hierarchical threat model evaluating remote, network, and physical attack vectors affecting imaging devices.
• It employs an empirical assessment of 15 devices to reveal encryption gaps and suboptimal update practices.
• The study recommends robust measures like encrypted data transmission, node authentication, and system hardening to enhance device security.

Security of Medical Cyber-Physical Systems: An Empirical Study on Imaging Devices

Medical cyber-physical systems are increasingly being integrated into hospital networks, with imaging devices playing a crucial role. This paper investigates the security issues surrounding these systems, focusing primarily on imaging devices. The paper introduces a threat model, potential attack vectors, and protective mechanisms while evaluating their effectiveness.

Threat Model and Attack Techniques

Attack Vectors

The research outlines three primary attack vectors:

1. Remote Server Poisoning: Imaging devices often connect to vendors' servers for tasks such as patch upgrades and remote control. If these servers are compromised, attackers can propagate malware and hijack network traffic.
2. Internal Network Penetration: Hospitals frequently provide public WiFi access, making internal networks susceptible to attacks such as ARP spoofing and social engineering that target imaging devices.
3. Physical Brute Force: Physical access to devices can allow attackers to directly extract sensitive data, highlighting the need for robust physical safeguards.

Techniques

Different attack techniques include port scanning, traffic analysis, and reverse engineering:

• Port Scanning: Identifies open ports and services, which can be exploited if not secured properly.
• Traffic Analysis: Observes data transmission; encrypted communications such as those via VPN can present challenges.
• Reverse Engineering: Analyzes software to uncover vulnerabilities, a time-intensive but impactful method.

  Figure 1: Visualization of a typical medical imaging device in a hospital network. Lines between devices/systems indicate data/control message transmission.

Protection Mechanisms

The paper thoroughly evaluates various protection mechanisms:

Network and System Safeguards

• Encrypted Data Transmission and Storage: Vital for maintaining confidentiality, implemented through methods like OpenVPN.
• Node Authentication: Uses DICOM's AE Title, IP address, and port for network management, though its efficacy depends on precise implementation.
• Physical Safeguards: Devices are often equipped with hardware locks to prevent unauthorized physical access.
• System Hardening: Involves software enhancements such as firewall installation and anti-virus applications.

  Figure 2: Evaluation of Protection Mechanisms.

Hierarchical Security System

To aid in evaluating device security, the paper proposes a hierarchical system, categorizing devices based on their protection level (e.g., CL1, CL2, CL3) with various safeguards' implementations. Higher levels require comprehensive features like full encryption and advanced identity authentication.

Empirical Evaluation

The paper assesses 15 medical devices, revealing significant gaps in encryption, auditing, and update practices across manufacturers. Some devices default to the minimal security configurations without meeting advanced requirements.

Conclusion

This empirical evaluation underscores the urgent need for improved security in medical cyber-physical systems, especially imaging devices. It introduces a structured hierarchical security assessment model, encouraging both vendors and regulatory bodies to adopt robust protection strategies for safeguarding sensitive healthcare data. Future research should explore innovative solutions for improving node authentication and system hardening practices.