@InProceedings{Schneider2013,
  author    = {Martin A. Schneider and J{\"u}rgen Gro{\ss}mann and Ina Kathrin Schieferdecker and Andrej Pietschker},
  booktitle = {Sixth {IEEE} International Conference on Software Testing, Verification and Validation, {ICST} 2013 Workshops Proceedings, Luxembourg, Luxembourg, March 18-22, 2013},
  title     = {Online Model-Based Behavioral Fuzzing},
  doi       = {10.1109/ICSTW.2013.61},
  pages     = {469--475},
  abstract  = {Fuzz testing or fuzzing is interface robustness testing by stressing the interface of a system under test (SUT) with invalid input data. It aims at finding security-relevant weaknesses in the implementation that may result in a crash of the system-under-test or anomalous behavior. Fuzzing means sending invalid input data to the SUT, the input space is usually huge. This is also true for behavioral fuzzing where invalid message sequences are submitted to the SUT. Because systems are getting more and more complex, testing a single invalid message sequence becomes more and more time consuming due to startup and initialization of the SUT. We present an approach to make the test execution for behavioral fuzz testing more efficient by generating test cases at runtime instead of before execution, focusing on interesting regions of a message sequence based on a previously conducted risk analysis and reducing the test space by integrating already retrieved test results in the test generation process.},
  file      = {:2013/Schneider2013 - Online Model Based Behavioral Fuzzing.pdf:PDF},
  keywords  = {program testing;security of data;SUT;anomalous behavior;fuzz testing;interface robustness testing;invalid message sequence testing;online model-based behavioral fuzzing;risk analysis;system under test;test case generation;test execution;Computer bugs;Protocols;Security;Software;Testing;Unified modeling language;Behavioral Fuzzing;Model-based Testing;Security Testing;Test Execution;Test Generation},
  type      = {conference},
  year      = {2013}
}