# TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN

arXiv: https://arxiv.org/abs/2409.12472 · HTML: https://mlsanity.com/paper/2409.12472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [What's Hidden in a Randomly Weighted Neural Network?](https://mlsanity.com/paper/1911.13299/) (2019)
- [IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection](https://mlsanity.com/paper/1809.02077/) (2018)
- [Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors](https://mlsanity.com/paper/2005.07519/) (2020)
- [Adversarial Machine Learning in Network Intrusion Detection Systems](https://mlsanity.com/paper/2004.11898/) (2020)
- [TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack](https://mlsanity.com/paper/2103.06297/) (2021)
- [Rallying Adversarial Techniques against Deep Learning for Network Security](https://mlsanity.com/paper/1903.11688/) (2019)
- [SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks](https://mlsanity.com/paper/1912.09303/) (2019)

## Abstract (arXiv)

With the development of artificial intelligence, neural networks play a key role in network intrusion detection systems (NIDS). Despite the tremendous advantages, neural networks are susceptible to adversarial attacks. To improve the reliability of NIDS, many research has been conducted and plenty of solutions have been proposed. However, the existing solutions rarely consider the adversarial attacks against recurrent neural networks (RNN) with time steps, which would greatly affect the application of NIDS in real world. Therefore, we first propose a novel RNN adversarial attack model based on feature reconstruction called Temporal adversarial Examples Attack Model (TEAM), which applied to time series data and reveals the potential connection between adversarial and time steps in RNN. That is, the past adversarial examples within the same time steps can trigger further attacks on current or future original examples. Moreover, TEAM leverages Time Dilation (TD) to effectively mitigates the effect of temporal among adversarial examples within the same time steps. Experimental results show that in most attack categories, TEAM improves the misjudgment rate of NIDS on both black and white boxes, making the misjudgment rate reach more than 96.68%. Meanwhile, the maximum increase in the misjudgment rate of the NIDS for subsequent original samples exceeds 95.57%.