研究用于训练基于机器学习的规避攻击的频谱欺骗损失度量
Investigating a Spectral Deception Loss Metric for Training Machine Learning-based Evasion Attacks
摘要：
对抗性规避攻击在各种机器学习应用程序中非常成功地导致性能低下。一种这样的应用是无线电频谱感测。虽然规避攻击在这一领域被证明是特别成功的，但它们这样做损害了信号的预期目的。更具体地说，对于感兴趣的现实世界应用，为了逃避窃听者而发送的所得到的扰动信号必须不偏离原始信号太远，更少的预期信息被破坏。作者和其他人最近的工作已经展示了一个攻击框架，该框架允许在逃避和通信的这些冲突目标之间进行智能平衡。然而，虽然这些方法考虑创建对抗信号以最小化通信降级，但它们已被证明是以牺牲信号的频谱形状为代价的。这将对抗性信号开放给窃听者的防御措施，例如过滤，这可能会使攻击无效。为了弥补这一点，这项工作引入了一个新的频谱欺骗损失度量，可以在训练过程中实施，以迫使频谱形状与原始信号更加一致。作为概念的初步证明，提出了各种方法，为这一拟议损失提供了一个起点。通过性能分析，它表明，这些技术是有效的控制对抗信号的形状。
英文摘要：
Adversarial evasion attacks have been very successful in causing poor performance in a wide variety of machine learning applications. One such application is radio frequency spectrum sensing. While evasion attacks have proven particularly successful in this area, they have done so at the detriment of the signal's intended purpose. More specifically, for real-world applications of interest, the resulting perturbed signal that is transmitted to evade an eavesdropper must not deviate far from the original signal, less the intended information is destroyed. Recent work by the authors and others has demonstrated an attack framework that allows for intelligent balancing between these conflicting goals of evasion and communication. However, while these methodologies consider creating adversarial signals that minimize communications degradation, they have been shown to do so at the expense of the spectral shape of the signal. This opens the adversarial signal up to defenses at the eavesdropper such as filtering, which could render the attack ineffective. To remedy this, this work introduces a new spectral deception loss metric that can be implemented during the training process to force the spectral shape to be more in-line with the original signal. As an initial proof of concept, a variety of methods are presented that provide a starting point for this proposed loss. Through performance analysis, it is shown that these techniques are effective in controlling the shape of the adversarial signal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
