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Focusing Learning-Based Testing Away from Known Weaknesses

  • Christian Fleischer
  • Jörg Denzinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10200)

Abstract

We present an extension to learning-based testing of systems for adversary-induced weaknesses that addresses the problem of repeated generation of known weaknesses. Our approach adds to the normally used fitness measure a component that computes the similarity of a test to known tests that revealed a weakness and uses this similarity to penalize new tests. We instantiated this idea to the testing of ad-hoc wireless networks using the IACL approach, more precisely to applications in precision agriculture, and our experiments show that our modification results in finding substantially different tests from the test(s) that we want to avoid.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

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