Abstract :
[en] Although the security testing of Web systems can be automated by generating
crafted inputs, solutions to automate the test oracle, i.e., vulnerability
detection, remain difficult to apply in practice. Specifically, though previous
work has demonstrated the potential of metamorphic testing, security failures
can be determined by metamorphic relations that turn valid inputs into
malicious inputs, metamorphic relations are typically executed on a large set
of inputs, which is time-consuming and thus makes metamorphic testing
impractical. We propose AIM, an approach that automatically selects inputs to
reduce testing costs while preserving vulnerability detection capabilities. AIM
includes a clustering-based black-box approach, to identify similar inputs
based on their security properties. It also relies on a novel genetic algorithm
to efficiently select diverse inputs while minimizing their total cost.
Further, it contains a problem-reduction component to reduce the search space
and speed up the minimization process. We evaluated the effectiveness of AIM on
two well-known Web systems, Jenkins and Joomla, with documented
vulnerabilities. We compared AIM's results with four baselines involving
standard search approaches. Overall, AIM reduced metamorphic testing time by
84% for Jenkins and 82% for Joomla, while preserving the same level of
vulnerability detection. Furthermore, AIM significantly outperformed all the
considered baselines regarding vulnerability coverage.
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