  Adversarial attacks have been widely studied in the field of computer vision
but their impact on network security applications remains an area of open
research. As IoT, 5G and AI continue to converge to realize the promise of the
fourth industrial revolution (Industry 4.0), security incidents and events on
IoT networks have increased. Deep learning techniques are being applied to
detect and mitigate many of such security threats against IoT networks.
Feedforward Neural Networks (FNN) have been widely used for classifying
intrusion attacks in IoT networks. In this paper, we consider a variant of the
FNN known as the Self-normalizing Neural Network (SNN) and compare its
performance with the FNN for classifying intrusion attacks in an IoT network.
Our analysis is performed using the BoT-IoT dataset from the Cyber Range Lab of
the center of UNSW Canberra Cyber. In our experimental results, the FNN
outperforms the SNN for intrusion detection in IoT networks based on multiple
performance metrics such as accuracy, precision, and recall as well as
multi-classification metrics such as Cohen's Kappa score. However, when tested
for adversarial robustness, the SNN demonstrates better resilience against the
adversarial samples from the IoT dataset, presenting a promising future in the
quest for safer and more secure deep learning in IoT networks.
