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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Integrating Safety and Security Engineering for Mission-Critical Systems

insights.sei.cmu.edu

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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, 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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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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RELATED IDEAS

Cloud computing is on-demand access, via the internet, to computing resources—applications, servers (physical servers and virtual servers), data storage, development tools, networking capabilities, and more—hosted at a remote data center managed by a cloud services provider (or CSP). The CSP makes these resources available for a monthly subscription fee or bills them according to usage.

Cloud computing has the following benefits:

  1. Lower IT costs
  2. Improved agility and Time-To-Value
  3. Better scaling.

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What is Cloud Computing?

ibm.com

10 SIMPLE WAYS TO STOP OVERTHINKING

When you get too concerned about every little detail, your thinking becomes muddled and your tension increases. Negativity has too much of a hold on you. It might become tough to act because of a lack of self-confidence.

Here are simple steps to avoid overthinking.

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10 Simple Ways You Can Stop Yourself From Overthinking

inc.com

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