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. Feed- forward 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 Cappaâ™s 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.

Adversarial samples, Feedforward Neural Networks (FNN), Internet of things (IoT), Intrusion Detection, Resilience, Self-normalizing Neural Networks (SNN)
2019 IEEE Global Communications Conference, GLOBECOM 2019
School of Information Technology

Ibitoye, O. (Olakunle), Shafiq, M.O, & Matrawy, A. (2019). Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. In 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings. doi:10.1109/GLOBECOM38437.2019.9014337