Integrating Safety and Security Engineering for Mission-Critical Systems - Deepstash
Integrating Safety and Security Engineering for Mission-Critical Systems

Integrating Safety and Security Engineering for Mission-Critical Systems

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Security Of Critical Systems

Critical systems must be both safe from inadvertent harm and secure from malicious actors. However, safety and security practices have historically evolved in isolation. Safety-critical systems, such as aircraft and medical devices, have long been analyzed for problems that could arise accidentally or from component degradation.

They have been considered standalone systems, however, that were impervious to security issues because they had no networking capabilities. 


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Modern Critical Systems

Modern critical systems, such as the CH-47F Chinook, TARDEC Autonomous Truck, and Little Bird, must be shown to be both safe and secure, but this is proving challenging as they are also increasingly complex. Indeed, the pace and scale of development of these systems make the traditional safety and security analyses cost-prohibitive. 

At the SEI, we are developing software and processes that use a system’s architecture as the starting point for assessing and improving safety and security. 


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An Integrated Solution

The SEI is developing an integrated approach to safety and security engineering, supported by an AADL-based workbench.

This approach:

  • Unifies safety and security analysis through a formalized taxonomy that is used to drive system verification via fault injection and simulation
  • Provides a design framework to combine safety and security mechanisms into a robust and resilient system architecture through continuous analytic verification.
  • Ensures traceability by linking machine-readable requirements to the tests that verify them and the system elements that implement them.


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Looking Ahead

As we continue our research into the integration of safety and security engineering for mission-critical systems, we are investigating the following questions:

Near-term—What assumptions underlying technologies that support increasing levels of autonomy (i.e., machine learning [ML], artificial intelligence [AI]) can we describe using AADL? 

Mid-term—How can models be used at runtime? What are the connections between static, design-time models and dynamic models used while a system is operating?

Long-term—To what extent can we use ML/AI to help develop models rather than the other way around?


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