Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This article comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker' s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. To the best of our knowledge, this work is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.

Automated summary

This article presents a comprehensive summary of recent research on adversarial attacks against security solutions based on machine learning techniques, highlighting the associated risks. The methods of adversarial attacks are characterized based on occurrence stage, attacker goals, and capabilities, while categorizing the applications of attack and defense methods in the cyber security domain. It also discusses the impact of recent progress in adversarial learning fields on future research directions in cyber security.