Sebastian Zillien

"Reliability Test Suite for Input Data Perturbations"

Many algorithms and machine learning models are designed, trained, tested and evaluated only with well-defined data, but in reality the inputs to such algorithms are often not as clean as one would hope. The goal of my work is to create a suite of test methods that can be applied to the inputs of different algorithms to evaluate the reliability of these algorithms in relation to disturbances of the input data. The first method that I evaluate is "added fuzziness of input variables" with the application scenario of timing covert channel detection. I evaluate the changes in performance of different detection approaches when confronted with such modified inputs. The scenario for this approach would be an attacker that intentionally uses less precise timings for their covert channel in order to evade detection approaches. The second application scenario for this method are track following and rating algorithms for particle detectors. The goal here is to simulate varying detector responses by modifying the energy deposition in different layers of the simulated detector.

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