# Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective

arXiv: https://arxiv.org/abs/2404.10796 · HTML: https://mlsanity.com/paper/2404.10796/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [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)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [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)
- [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)
- [Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)](https://mlsanity.com/paper/2112.08442/) (2021)
- [Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.06664/) (2023)
- [Adversarial Examples - A Complete Characterisation of the Phenomenon](https://mlsanity.com/paper/1810.01185/) (2018)
- [Rallying Adversarial Techniques against Deep Learning for Network Security](https://mlsanity.com/paper/1903.11688/) (2019)
- [Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems](https://mlsanity.com/paper/2004.05005/) (2020)
- [Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model](https://mlsanity.com/paper/2308.00074/) (2023)
- [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)
- [Evaluating Resilience of Encrypted Traffic Classification Against Adversarial Evasion Attacks](https://mlsanity.com/paper/2105.14564/) (2021)
- [A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks](https://mlsanity.com/paper/2308.00077/) (2023)

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

- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS](https://mlsanity.com/paper/2506.20576/) (2025)
- [A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic](https://mlsanity.com/paper/2607.17105/) (2026)

## Abstract (arXiv)

The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial attacks is one of the major issues. In adversarial machine learning, malicious users try to fool the deep learning model by inserting adversarial perturbation inputs into the model during its training or testing phase. Subsequently, it reduces the model confidence score and results in incorrect classifications. The novel key contribution of the research is to empirically test the black-box adversarial transferability phenomena in cyber attack detection systems. It indicates that the adversarial perturbation input generated through the surrogate model has a similar impact on the target model in producing the incorrect classification. To empirically validate this phenomenon, surrogate and target models are used. The adversarial perturbation inputs are generated based on the surrogate-model for which the hacker has complete information. Based on these adversarial perturbation inputs, both surrogate and target models are evaluated during the inference phase. We have done extensive experimentation over the CICDDoS-2019 dataset, and the results are classified in terms of various performance metrics like accuracy, precision, recall, and f1-score. The findings indicate that any deep learning model is highly susceptible to adversarial attacks, even if the attacker does not have access to the internal details of the target model. The results also indicate that white-box adversarial attacks have a severe impact compared to black-box adversarial attacks. There is a need to investigate and explore adversarial defence techniques to increase the robustness of the deep learning models against adversarial attacks.