Model checking for software security properties
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This paper presents a portion of an overall research project on the generation of a software security assessment instrument to aid developers in assessing and assuring the security of software in the development and maintenance lifecycles.
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The proposed usage of Rapture/SP2 in this paper is to identify 'smaller' portions of a system that are more likely to contribute to hazardous behavior.
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This paper proposes an approach that mitigates the problem of verification of network security system properties over concurrent processes.
UML use cases conceptually identify function points or major requirements that a software system must satisfy. Sequence diagrams expand each use case to show in temporal sequence a more detailed notion of intended system behavior.
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Following the trend of creating flight code with state chart modeling and auto-code generation, NASA's Deep Impact (DI) project uses Stateflow for its Fault Protection Flight Software development.
The difficulty of how to test large systems, such as the one on board a NASA robotic remote explorer (RRE) vehicle, is fundamentally a search issue: the global state space representing all possible has yet to be solved, even after many decades of work. Randomized algorithms have been known to outperform their deterministic counterparts for search problems representing a wide range of applications. In the case study presented here, the LURCH randomized algorithm proved to be adequate to the task of testing a NASA RRE vehicle. LURCH found all the errors found by an earlier analysis of a more complete method (SPIN). Our empirical results are that LURCH can scale to much larger models than standard model checkers like SMV and SPIN. Further, the LURCH analysis was simpler than the SPIN analysis. The simplicity and scalability of LURCH are two compelling reasons for experimenting further with this tool.
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Abstract not provided.
We report the Nearly Autonomous Management And Control System (NAMAC) is a comprehensive control system to assist plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for making reasonable recommendations during complex loss of flow (LOF) scenarios with a validated EBR-II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects. The performance of each NAMAC component is assessed, while the capability of NAMAC is demonstrated and evaluated in a class of LOF scenarios.