# Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI

arXiv: https://arxiv.org/abs/2405.18753 · HTML: https://mlsanity.com/paper/2405.18753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 63 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 14 of 14)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [SoK: Certified Robustness for Deep Neural Networks](https://mlsanity.com/paper/2009.04131/) (2020)
- [An introduction to Docker for reproducible research, with examples from the R environment](https://mlsanity.com/paper/1410.0846/) (2014)
- [Towards Training Reproducible Deep Learning Models](https://mlsanity.com/paper/2202.02326/) (2022)
- [eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys](https://mlsanity.com/paper/1702.08568/) (2017)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues](https://mlsanity.com/paper/2302.12691/) (2023)
- [A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System](https://mlsanity.com/paper/2312.03245/) (2023)
- [Towards Inferential Reproducibility of Machine Learning Research](https://mlsanity.com/paper/2302.04054/) (2023)

## Built on by (most cited first, 2 of 2)

- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers](https://mlsanity.com/paper/2603.06862/) (2026)

## Abstract (arXiv)

In the rapidly evolving field of cybersecurity, ensuring the reproducibility of AI-driven research is critical to maintaining the reliability and integrity of security systems. This paper addresses the reproducibility crisis within the domain of adversarial robustness -- a key area in AI-based cybersecurity that focuses on defending deep neural networks against malicious perturbations. Through a detailed case study, we attempt to validate results from prior work on certified robustness using the VeriGauge toolkit, revealing significant challenges due to software and hardware incompatibilities, version conflicts, and obsolescence. Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation to ensure the reproducibility of AI models deployed in critical cybersecurity applications. By tackling these reproducibility challenges, we aim to contribute to the broader discourse on securing AI systems against advanced persistent threats, enhancing network and IoT security, and protecting critical infrastructure. This work advocates for a concerted effort within the research community to prioritize reproducibility, thereby strengthening the foundation upon which future cybersecurity advancements are built.