# Constrained Adversarial Learning for Automated Software Testing: a literature review

arXiv: https://arxiv.org/abs/2303.07546 · HTML: https://mlsanity.com/paper/2303.07546/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 164 references that are in the map.
- First posted on arXiv 14 Mar 2023.

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

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [HP-GAN: Probabilistic 3D human motion prediction via GAN](https://mlsanity.com/paper/1711.09561/) (2017)
- [Deep Cross-Modal Audio-Visual Generation](https://mlsanity.com/paper/1704.08292/) (2017)
- [Content and Style Aware Generation of Text-line Images for Handwriting Recognition](https://mlsanity.com/paper/2204.05539/) (2022)
- [Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations](https://mlsanity.com/paper/1909.02210/) (2019)
- [Adversarial Machine Learning in Network Intrusion Detection Systems](https://mlsanity.com/paper/2004.11898/) (2020)
- [FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments](https://mlsanity.com/paper/1909.10480/) (2019)
- [Automatic Web Testing using Curiosity-Driven Reinforcement Learning](https://mlsanity.com/paper/2103.06018/) (2021)
- [AMENet: Attentive Maps Encoder Network for Trajectory Prediction](https://mlsanity.com/paper/2006.08264/) (2020)
- [ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems](https://mlsanity.com/paper/2003.05631/) (2020)
- [Conditional Generation of Medical Images via Disentangled Adversarial Inference](https://mlsanity.com/paper/2012.04764/) (2020)
- [Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection](https://mlsanity.com/paper/2203.04234/) (2022)
- [Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1901.06219/) (2019)
- [Decision-Aware Conditional GANs for Time Series Data](https://mlsanity.com/paper/2009.12682/) (2020)
- [Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks](https://mlsanity.com/paper/2204.00144/) (2022)
- [Coercing Machine Learning to Output Physically Accurate Results](https://mlsanity.com/paper/1910.09671/) (2019)
- [Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification](https://mlsanity.com/paper/2301.13122/) (2023)
- [Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN](https://mlsanity.com/paper/2206.12076/) (2022)
- [High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder](https://mlsanity.com/paper/2006.00877/) (2020)
- [Uncertainty-Driven Black-Box Test Data Generation](https://mlsanity.com/paper/1608.03181/) (2016)
- [Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior](https://mlsanity.com/paper/2006.09267/) (2020)
- [Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1902.09842/) (2019)

## Built on by (most cited first, 6 of 6)

- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection](https://mlsanity.com/paper/2402.16912/) (2024)
- [Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection](https://mlsanity.com/paper/2404.04188/) (2024)
- [Efficient Network Traffic Feature Sets for IoT Intrusion Detection](https://mlsanity.com/paper/2406.08042/) (2024)
- [FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer](https://mlsanity.com/paper/2407.14361/) (2024)
- [TestLab: An Intelligent Automated Software Testing Framework](https://mlsanity.com/paper/2306.03602/) (2023)

## Abstract (arXiv)

It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickly analyze many lines of code and detect vulnerabilities by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial machine learning methods, so there could be significant benefits to the integration of these methods in testing tools to identify possible attack vectors. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation approaches applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance their software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found approaches were systematized, and the advantages and limitations of those specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to automate the testing tools with data generated by adversarial attacks.