A LearningBased Framework for Automatic Parameterized Verification Parameterized verification is shown to be a complicated and undecidable problem. The challenge of parameterized verification lies in how to construct appropriate invariants. Designing algorithms to find such invariants automatically has become an active research area since the last decade. With the advent of some recent works, automatically finding invariants has become possible, but most of these invariants are unreadable, making them difficult to be understood by protocol designers and researchers. Therefore, we propose an automatic framework that learns a set of readable and simple invariants to support in protocol design. It takes advantage of association rule learning, and combines the learning algorithm with parameterized verification. It is noteworthy that the gap between machine learning algorithms and parameterized verification seems to be huge, as they rely on statistical learning and symbolic reasoning, respectively. Our framework, however, builds a bridge through association rules and invariants, making their combination possible. Besides, we also propose an invariantguided strengthening paradigm, providing an innovative perspective to existing abstractionstrengthening methods. Our framework has been successfully applied to several benchmarks, including an industrialscale protocol FLASH.

Synthesizing Mutable Configurations: Setting up Systems for Success Numerous devices, from network switches and servers to industrial control systems, can be unreliable if they are not configured properly. Even if a deviceu0027s implementation has been proven correct, it must still be configured to meet the specific functional and security requirements of its stakeholders. However, manual configuration remains labor intensive and errorprone even for experts. Automated configuration synthesis presents a promising way forward. Unfortunately, as we show, existing counterexampleguided algorithms can perform poorly if the system model allows configuration changes during execution. Yet disallowing such changes can hide significant problems, such as privilege escalation. We present a new synthesis algorithm that exploits structure inherent in statemachine models where the system configuration changes. We implement it using the Kodkod relational model finder, and show that it favorably solves a number of configurationsynthesis tasks.