A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks
This is an incremental survey addressing the vulnerability of supervised learning algorithms in cybersecurity to adversarial attacks.
The paper surveys game-theoretic approaches to defend against adversarial attacks in machine learning for cybersecurity, where attackers manipulate training data to cause classification errors, and it outlines open problems and future research directions.
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.