# Rallying Adversarial Techniques against Deep Learning for Network Security

arXiv: https://arxiv.org/abs/1903.11688 · HTML: https://mlsanity.com/paper/1903.11688/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack](https://mlsanity.com/paper/2103.06297/) (2021)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System](https://mlsanity.com/paper/2310.03334/) (2023)
- [TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2210.15700/) (2022)
- [A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System](https://mlsanity.com/paper/2312.03245/) (2023)
- [Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.07003/) (2023)
- [Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective](https://mlsanity.com/paper/2404.10796/) (2024)
- [L-XAIDS: A LIME-based eXplainable AI framework for Intrusion Detection Systems](https://mlsanity.com/paper/2508.17244/) (2025)
- [NetDiffuser: Deceiving DNN-Based Network Attack Detection Systems with Diffusion-Generated Adversarial Traffic](https://mlsanity.com/paper/2603.08901/) (2026)
- [TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN](https://mlsanity.com/paper/2409.12472/) (2024)
- [Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries](https://mlsanity.com/paper/2502.05367/) (2025)

## Abstract (arXiv)

Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning approaches for use in network intrusion detection systems. These methods have achieved superior performance against conventional network attacks, which enable the deployment of practical security systems to unique and dynamic sectors. Adversarial machine learning, unfortunately, has recently shown that deep learning models are inherently vulnerable to adversarial modifications on their input data. Because of this susceptibility, the deep learning models deployed to power a network defense could in fact be the weakest entry point for compromising a network system. In this paper, we show that by modifying on average as little as 1.38 of the input features, an adversary can generate malicious inputs which effectively fool a deep learning based NIDS. Therefore, when designing such systems, it is crucial to consider the performance from not only the conventional network security perspective but also the adversarial machine learning domain.