The SEI is developing an integrated approach to safety and security engineering, supported by an AADL-based workbench.
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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.
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.
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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Cloud computing has the following benefits:
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.
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