mlsanityData to 7 Oct 2026

Constrained Adversarial Learning for Automated Software Testing: a literature review

arXiv 2303.07546 ↗First posted 14 Mar 2023cs.SE cs.LGcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 164 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

22 of 164 references.

References · 22

  1. 2014Conditional Generative Adversarial Netscited by 2,152
  2. 2017HP-GAN: Probabilistic 3D human motion prediction via GANcited by 176
  3. 2017Deep Cross-Modal Audio-Visual Generationcited by 63
  4. 2022Content and Style Aware Generation of Text-line Images for Handwriting Recognitioncited by 35
  5. 2019Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulationscited by 32
  6. 2020Adversarial Machine Learning in Network Intrusion Detection Systemscited by 25
  7. 2019FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environmentscited by 23
  8. 2021Automatic Web Testing using Curiosity-Driven Reinforcement Learningcited by 19
  9. 2020AMENet: Attentive Maps Encoder Network for Trajectory Predictioncited by 17
  10. 2020ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systemscited by 13
  11. 2020Conditional Generation of Medical Images via Disentangled Adversarial Inferencecited by 13
  12. 2022Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detectioncited by 13
10 more
  1. 2019Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networkscited by 12
  2. 2020Decision-Aware Conditional GANs for Time Series Datacited by 11
  3. 2022Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networkscited by 11
  4. 2019Coercing Machine Learning to Output Physically Accurate Resultscited by 10
  5. 2023Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classificationcited by 10
  6. 2022Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGANcited by 8
  7. 2020High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencodercited by 5
  8. 2016Uncertainty-Driven Black-Box Test Data Generationcited by 4
  9. 2020Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behaviorcited by 3
  10. 2019Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networkscited by 1

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 4.

6 papers.

  1. 2023SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detectioncited by 9
  2. 2024An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detectioncited by 3
  3. 2024Reliable Feature Selection for Adversarially Robust Cyber-Attack Detectioncited by 3
  4. 2024Efficient Network Traffic Feature Sets for IoT Intrusion Detectioncited by 2
  5. 2024FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzercited by 2
  6. 2023TestLab: An Intelligent Automated Software Testing Frameworkcited by 1