基于深度学习的对抗和无意碰撞检测

Proceedings of the ACM Workshop on Wireless Security and Machine Learning Pub Date : 2019-05-15 DOI:10.1145/3324921.3328784

H. Nguyen, T. Vo-Huu, Triet Vo Huu, G. Noubir

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引用次数: 4

摘要

我们介绍了一套技术来实现从计算机视觉到射频频谱分析的迁移学习。在本文中，我们证明了这种方法在使用VGG-16扩展对抗性和非故意通信碰撞检测的学习，准确性和效率方面的有效性。我们在DARPA频谱协作挑战(SC2)数据集上实现了高精度(检测到94%的碰撞)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Towards Adversarial and Unintentional Collisions Detection Using Deep Learning

We introduce a set of techniques to achieve transfer learning from computer vision to RF spectrum analysis. In this paper, we demonstrate the usefulness of this approach to scale the learning, accuracy, and efficiency of detection of adversarial and unintentional communications collisions using VGG-16. We achieve high accuracy (94% collisions detected) on a DARPA Spectrum Collaboration Challenge (SC2) dataset.

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Proceedings of the ACM Workshop on Wireless Security and Machine Learning

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