  Ensuring safety and explainability of machine learning (ML) is a topic of
increasing relevance as data-driven applications venture into safety-critical
application domains, traditionally committed to high safety standards that are
not satisfied with an exclusive testing approach of otherwise inaccessible
black-box systems. Especially the interaction between safety and security is a
central challenge, as security violations can lead to compromised safety. The
contribution of this paper to addressing both safety and security within a
single concept of protection applicable during the operation of ML systems is
active monitoring of the behaviour and the operational context of the
data-driven system based on distance measures of the Empirical Cumulative
Distribution Function (ECDF). We investigate abstract datasets (XOR, Spiral,
Circle) and current security-specific datasets for intrusion detection
(CICIDS2017) of simulated network traffic, using distributional shift detection
measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling,
Wasserstein and mixed Wasserstein-Anderson-Darling measures. Our preliminary
findings indicate that the approach can provide a basis for detecting whether
the application context of an ML component is valid in the safety-security. Our
preliminary code and results are available at
https://github.com/ISorokos/SafeML.
