# A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks

arXiv: https://arxiv.org/abs/1912.02258 · HTML: https://mlsanity.com/paper/1912.02258/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective](https://mlsanity.com/paper/2101.03042/) (2021)
- [Sequential Manipulation Against Rank Aggregation: Theory and Algorithm](https://mlsanity.com/paper/2407.01916/) (2024)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [The Art of Deception: Robust Backdoor Attack using Dynamic Stacking of Triggers](https://mlsanity.com/paper/2401.01537/) (2024)
- [On the Convergence of Fictitious Play: A Decomposition Approach](https://mlsanity.com/paper/2205.01469/) (2022)
- [Classification under strategic adversary manipulation using pessimistic bilevel optimisation](https://mlsanity.com/paper/2410.20284/) (2024)
- [Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey](https://mlsanity.com/paper/2101.10121/) (2021)
- [Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2302.00537/) (2023)
- [Countering adversarial evasion in regression analysis](https://mlsanity.com/paper/2509.22113/) (2025)
- [Adversarial training with restricted data manipulation](https://mlsanity.com/paper/2510.03254/) (2025)
- [On the Trade-Off Between Transparency and Security in Adversarial Machine Learning](https://mlsanity.com/paper/2511.11842/) (2025)

## Abstract (arXiv)

Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks where a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable to use and leave critical systems vulnerable to cybersecurity attacks. Our paper provides a detailed survey of the state-of-the-art techniques that are used to make a machine learning algorithm robust against adversarial attacks using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning-based systems more robust and reliable for cybersecurity tasks.