Machine Learning (ML) models are susceptible to adversarial samples that appear as normal samples but have some imperceptible noise added to them with the intention of misleading a trained classifier and misclassifying the input. Adversarial Machine Learning (AML) was initially coined following upon researchers pointing out certain blind spots in image classifiers in computer vision field which were exploited by these adversarial samples to deceive the model. Although this has been investigated remarkably in computer vision, the impact of AML in wireless and mobile systems has recently attracted attention. Wireless and mobile networks have intensely benefited from the application of ML classifiers to detect network traffic anomalies and malware detection. However, ML detectors themselves can be exfiltrated/evaded by the samples carefully designed by attackers, raising security concerns for ML-based network applications. Thus, it is crucial to detect such samples to safeguard the network. This survey article presents a systematic mapping and a comprehensive literature review on AML to wireless and mobile systems from physical layer to network and application layers. The article reviews the state-of-the-art AML approaches in the generation and detection of adversarial samples. The samples can be generated by adversarial models such as Generative Adversarial Networks (GANS) and techniques such as Fast Gradient Sign Method (FGSM). The samples can be detected by adversarial models acting as classifiers or ML classifiers reinforced with knowledge on how to detect such samples. For each approach, a high-level overview is provided alongside its impact on solving the problems in wireless and mobile settings. Furthermore, this article provides detailed discussions to highlight the open issues and challenges faced by these approaches, as well as research opportunities which can be of interest to the researchers and developers in Artificial Intelligence (AI)-driven wireless and mobile networking.

中文翻译：

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对抗性机器学习：无线和移动系统的最新技术和挑战的多层回顾

机器学习 (ML) 模型容易受到对抗性样本的影响，这些对抗性样本显示为正常样本，但添加了一些难以察觉的噪声，目的是误导经过训练的分类器并对输入进行错误分类。对抗性机器学习（AML）最初是在研究人员指出计算机视觉领域的图像分类器中的某些盲点被这些对抗性样本利用来欺骗模型之后创造出来的。尽管这已经在计算机视觉中得到了显着的研究，但 AML 在无线和移动系统中的影响最近引起了人们的关注。无线和移动网络极大地受益于 ML 分类器的应用，以检测网络流量异常和恶意软件检测。然而，ML 检测器本身可以被攻击者精心设计的样本泄露/规避，从而引发基于 ML 的网络应用程序的安全问题。因此，检测此类样本以保护网络至关重要。这篇调查文章介绍了从物理层到网络和应用层的 AML 到无线和移动系统的系统映射和全面的文献回顾。本文回顾了对抗样本的生成和检测中最先进的 AML 方法。样本可以通过生成对抗网络 (GANS) 等对抗模型和快速梯度符号法 (FGSM) 等技术生成。样本可以通过充当分类器的对抗模型或通过有关如何检测此类样本的知识来加强的 ML 分类器来检测。对于每种方法，除了它对解决无线和移动环境中的问题的影响之外，还提供了一个高级概述。此外，本文提供了详细的讨论，以突出这些方法面临的开放性问题和挑战，以及人工智能 (AI) 驱动的无线和移动网络的研究人员和开发人员可能感兴趣的研究机会。