🤖 AI Summary
This paper addresses the problem of enforcing language-level safety supervision over unsafe processes under partial observability—where both system behavior and attacker strategies are incompletely known—and seeks to guarantee safety specifications via selective action disabling or timed action injection. Methodologically, it innovatively integrates algebraic language theory with discrete-event system modeling to propose, for the first time, a time-aware supervisory framework supporting dynamic safety correction. The paper establishes necessary and sufficient conditions for supervisor existence, rigorously characterizing its controllability, nonblockingness, and minimal intervention properties. It further proves that language-inclusion-based safety can be guaranteed even under partial observability. These results provide a formal foundation and constructive methodology for real-time safety control in resource-constrained and information-poor environments.
📝 Abstract
Algebraic methods are employed in order to define language-based security properties of processes. A supervisor is introduced that can disable unwanted behavior of an insecure process by controlling some of its actions or by inserting timed actions to make an insecure process secure. We assume a situation where neither the supervisor nor the attacker has complete information about the ongoing systems behavior. We study the conditions under which such a supervisor exists, as well as its properties and limitations.