Today: Need NF Models for Testing and Verification
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Limitation of Handwritten Model: Inaccuracy
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Challenges on Large Configuration Space
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We Can Compose Models of Individual Rules
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Use Symbolic Models to represent Large Sets
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Exploit Independence to Create an Ensemble of FSMS
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Challenges on Inferring NF Behavior
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Background on L* for Black-box FSM Inference
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Practical Challenges of Applying L* for an NF
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Generating Input Alphabet to handle Large Traffic Space
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Learning the State Granularity
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Alembic Workflow: Offline
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Evaluation Summary
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Evaluation Setup
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Firewall Case Study: Untangle Firewall
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Limitations and Future Work
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Conclusions: Alembic can accurately model stateful NFS
Description:
Explore a conference talk on Alembic, an automated model inference system for stateful network functions. Learn about the challenges of creating accurate models for complex network functions and how Alembic addresses these issues. Discover how the system uses symbolic finite-state machine representations and an ensemble approach to generate behavioral models for given configurations. Gain insights into the practical applications of Alembic in network testing and verification, and understand its potential impact on improving the accuracy and efficiency of network function modeling. Examine the evaluation results, including a case study on the Untangle Firewall, and consider the limitations and future directions of this innovative approach to network function modeling.
Alembic - Automated Model Inference for Stateful Network Functions