Source code and data relevant for the paper 'Model Learning and Model Checking of SSH Implementations'
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The dataset contains source code and data relevant for the paper "Model Learning and Model Checking of SSH Implementations".Paper url: https://dl.acm.org/citation.cfm?id=3092289PDF url: https://www.cs.ru.nl/E.Poll/papers/learning_ssh.pdfIn this work, we use automata learning with abstraction to infer models of 3 SSH server implementations (BitVise, DropBear, OpenSSH). We then verify properties on these models using the NuSMV model checker. The dataset comprises the software components of our experimental setup apart from the actual implementations, some useful scripts and the learned models and associated experimental logs.In more concrete terms, the dataset contains:- the learner (setup) implementation - Java code for the setup built around LearnLib to perform learning over sockets- the mapper implementation - Python code for the altered Paramiko SSH implementation- experimental results - learned Mealy Machine models for 3 SSH server implementations (BitVise, OpenSSH, DropBear)- experimental data - contain additional information not included in experimental results- various Python scripts- .dot to .smv converter - Java program for converting Mealy Machine .dot models to .smv …
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