Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems

TL;DR

This study examines how increasing the depth of deep neural networks influences their robustness against adversarial attacks in network intrusion detection systems, revealing that deeper networks may be less robust in this domain.

Contribution

It provides a comparative analysis of DNN depth effects on adversarial robustness in NIDS versus computer vision, highlighting domain-specific impacts.

Findings

Deeper DNNs do not improve robustness in NIDS.

In NIDS, increased depth can significantly reduce robustness.

In computer vision, depth has a modest effect on robustness.

Abstract

Adversarial attacks pose significant challenges to Machine Learning (ML) systems and especially Deep Neural Networks (DNNs) by subtly manipulating inputs to induce incorrect predictions. This paper investigates whether increasing the layer depth of deep neural networks affects their robustness against adversarial attacks in the Network Intrusion Detection System (NIDS) domain. We compare the adversarial robustness of various deep neural networks across both \ac{NIDS} and computer vision domains (the latter being widely used in adversarial attack experiments). Our experimental results reveal that in the NIDS domain, adding more layers does not necessarily improve their performance, yet it may actually significantly degrade their robustness against adversarial attacks. Conversely, in the computer vision domain, adding more layers exhibits a more modest impact on robustness. These findings can guide the development of robust neural networks for (NIDS) applications and highlight the unique characteristics of network security domains within the (ML) landscape.