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

TL;DR

This paper surveys game theoretic methods to enhance the robustness of machine learning algorithms against adversarial attacks in cybersecurity, highlighting current techniques, challenges, and future research directions.

Contribution

It provides a comprehensive review of game theoretic approaches to defend machine learning models from adversarial threats in cybersecurity.

Findings

Game theory-based methods improve adversarial robustness.

Identification of open challenges in current approaches.

Discussion of future research directions in the field.

Abstract

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.