# Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks

## TL;DR

This paper compares the performance and adversarial robustness of Feedforward Neural Networks and Self-normalizing Neural Networks for intrusion detection in IoT networks, highlighting the trade-offs between accuracy and security.

## Contribution

It introduces a comparison between FNN and SNN in IoT intrusion detection, emphasizing SNN's superior robustness against adversarial attacks.

## Key findings

- FNN outperforms SNN in accuracy, precision, and recall.
- SNN shows better resilience to adversarial samples.
- Results suggest a trade-off between detection performance and robustness.

## Abstract

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.