# Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation

arXiv: https://arxiv.org/abs/2407.08838 · HTML: https://mlsanity.com/paper/2407.08838/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Accounting for Variance in Machine Learning Benchmarks](https://mlsanity.com/paper/2103.03098/) (2021)
- [Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization](https://mlsanity.com/paper/1708.08689/) (2017)
- [SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics](https://mlsanity.com/paper/2104.11315/) (2021)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Deep Structured Energy Based Models for Anomaly Detection](https://mlsanity.com/paper/1605.07717/) (2016)
- [Neural Transformation Learning for Deep Anomaly Detection Beyond Images](https://mlsanity.com/paper/2103.16440/) (2021)
- [Is feature selection secure against training data poisoning?](https://mlsanity.com/paper/1804.07933/) (2018)
- [On Certifying Robustness against Backdoor Attacks via Randomized Smoothing](https://mlsanity.com/paper/2002.11750/) (2020)
- [A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms](https://mlsanity.com/paper/2204.09825/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Novel Contrastive Loss for Zero-Day Network Intrusion Detection](https://mlsanity.com/paper/2601.09902/) (2026)
- [Contrastive Self-Supervised Network Intrusion Detection using Augmented Negative Pairs](https://mlsanity.com/paper/2509.06550/) (2025)
- [Few-Shot Network Intrusion Detection Using Online Triplet Mining](https://mlsanity.com/paper/2605.17530/) (2026)
- [Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization](https://mlsanity.com/paper/2610.02345/) (2026)

## Abstract (arXiv)

Deep learning (DL) has emerged as a crucial tool in network anomaly detection (NAD) for cybersecurity. While DL models for anomaly detection excel at extracting features and learning patterns from data, they are vulnerable to data contamination -- the inadvertent inclusion of attack-related data in training sets presumed benign. This study evaluates the robustness of six unsupervised DL algorithms against data contamination using our proposed evaluation protocol. Results demonstrate significant performance degradation in state-of-the-art anomaly detection algorithms when exposed to contaminated data, highlighting the critical need for self-protection mechanisms in DL-based NAD models. To mitigate this vulnerability, we propose an enhanced auto-encoder with a constrained latent representation, allowing normal data to cluster more densely around a learnable center in the latent space. Our evaluation reveals that this approach exhibits improved resistance to data contamination compared to existing methods, offering a promising direction for more robust NAD systems.