  Deep Neural Networks (DNN) have been widely adopted in self-organizing
networks (SON) for automating different networking tasks. Recently, it has been
shown that DNN lack robustness against adversarial examples where an adversary
can fool the DNN model into incorrect classification by introducing a small
imperceptible perturbation to the original example. SON is expected to use DNN
for multiple fundamental cellular tasks and many DNN-based solutions for
performing SON tasks have been proposed in the literature have not been tested
against adversarial examples. In this paper, we have tested and explained the
robustness of SON against adversarial example and investigated the performance
of an important SON use case in the face of adversarial attacks. We have also
generated explanations of incorrect classifications by utilizing an explainable
artificial intelligence (AI) technique.
