# Adaptive Intrusion Detection System using Transformer-Based Neural Networks and Continual Learning Approach with Adversarial Investigation

arXiv: https://arxiv.org/abs/2608.04602 · HTML: https://mlsanity.com/paper/2608.04602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2106.09380/) (2021)
- [Adaptive Memory Replay for Continual Learning](https://mlsanity.com/paper/2404.12526/) (2024)
- [Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks](https://mlsanity.com/paper/2002.07111/) (2020)
- [Persistent Backdoor Attacks in Continual Learning](https://mlsanity.com/paper/2409.13864/) (2024)
- [Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design](https://mlsanity.com/paper/2305.16160/) (2023)

## Abstract (arXiv)

Network intrusion detection systems (IDS) trained on fixed traffic snapshots decay silently after deployment as threat distributions shift. Fine-tuning models on new attacks triggers catastrophic forgetting, while retraining from scratch is computationally infeasible. Replay-based continual learning counters this, but existing methods unrealistically confine benign traffic to a single early task and ignore the replay buffer as a potential attack surface. To address this, we present an adaptive IDS framework coupling a tabular transformer encoder with a class balanced experience replay buffer that replays benign traffic at every update to stabilize decision boundaries. We introduce the class-instance incremental (CII) scenario where benign flows reappear alongside new attacks as a more faithful stress test, and probe the buffer with overt label flipping and stealthy backdoor poisoning attacks. On the CICIDS2017 benchmark, our framework achieved 0.9994 accuracy under the traditional class incremental setup and 0.9989 under CII, with negligible forgetting, drastically outperforming sequential fine-tuning (0.0052), EWC (0.0324), LwF (0.0699), and iCaRL (0.8770) baselines. While injecting benign traffic into every experience proves essential for preventing forgetting, the replay buffer introduces critical vulnerabilities. Label-flipping collapses the model entirely (0.0053 accuracy at a 1% budget), and the backdoor maintains 0.97 overall accuracy while driving the attack success rate on trigger flows to 95%, evading standard monitoring. Ultimately, while a modest replay budget recovers near-joint-training performance, ensuring buffer integrity emerges as a strict operational requirement.