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

arXiv: https://arxiv.org/abs/2510.19761 · HTML: https://mlsanity.com/paper/2510.19761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks](https://mlsanity.com/paper/2110.03825/) (2021)
- [Deep vs. shallow networks : An approximation theory perspective](https://mlsanity.com/paper/1608.03287/) (2016)
- [Architectural Adversarial Robustness: The Case for Deep Pursuit](https://mlsanity.com/paper/2011.14427/) (2020)
- [The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey](https://mlsanity.com/paper/1911.02621/) (2019)
- [Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones](https://mlsanity.com/paper/2306.05494/) (2023)
- [Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness](https://mlsanity.com/paper/2410.18556/) (2024)

## Built on by (most cited first, 1 of 1)

- [A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?](https://mlsanity.com/paper/2605.18666/) (2026)

## Abstract (arXiv)

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 acNIDS 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.