Adversarial Machine Learning in Wireless Communications using RF Data: A Review
Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML for next-generation wireless communications are discussed.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Review of Machine Learning Applications in Wireless Communications
This paper looks at various aspects of Machine Learning (ML) applications in wireless communication technologies, focusing mainly on fifth-generation (5G) and millimeter wave (mmWave) technologies. This paper includes th…
BIG-bench Machine LearningNon-Coherent and Backscatter Communications: Enabling Ultra-Massive Connectivity in 6G Wireless Networks
With the commencement of the 5G of wireless networks, researchers around the globe have started paying their attention to the imminent challenges that may emerge in the beyond 5G (B5G) era. Various revolutionary technolo…
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved …
BIG-bench Machine LearningCloud ComputingFederated LearningManagement+1How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy
We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is present or not. There exists one transmitter t…
BIG-bench Machine LearningAdversarial Machine Learning for 5G Communications Security
Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter w…
BIG-bench Machine LearningGenerative Adversarial Network