# Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2112.12095 · HTML: https://mlsanity.com/paper/2112.12095/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [On the (Statistical) Detection of Adversarial Examples](https://mlsanity.com/paper/1702.06280/) (2017)
- [Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction](https://mlsanity.com/paper/1911.11616/) (2019)
- [Adversarial Training for Deep Learning-based Intrusion Detection Systems](https://mlsanity.com/paper/2104.09852/) (2021)
- [Efficient Intrusion Detection Using Evidence Theory](https://mlsanity.com/paper/2103.08585/) (2021)

## Built on by (most cited first, 3 of 3)

- [Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.06664/) (2023)
- [TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2210.15700/) (2022)
- [Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networks](https://mlsanity.com/paper/2603.23438/) (2026)

## Abstract (arXiv)

In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifically are vulnerable to adversarial attacks where the attacker attempts to fool models by supplying deceptive input. Research in computer vision, where this vulnerability was first discovered, has shown that adversarial images designed to fool a specific model can deceive other machine learning models. In this paper, we investigate the transferability of adversarial network traffic against multiple machine learning-based intrusion detection systems. Furthermore, we analyze the robustness of the ensemble intrusion detection system, which is notorious for its better accuracy compared to a single model, against the transferability of adversarial attacks. Finally, we examine Detect & Reject as a defensive mechanism to limit the effect of the transferability property of adversarial network traffic against machine learning-based intrusion detection systems.