An adversarial machine learning approach is introduced to launch jamming
attacks on wireless communications and a defense strategy is presented. A
cognitive transmitter uses a pre-trained classifier to predict the current
channel status based on recent sensing results and decides whether to transmit
or not, whereas a jammer collects channel status and ACKs to build a deep
learning classifier that reliably predicts the next successful transmissions
and effectively jams them. This jamming approach is shown to reduce the
transmitter's performance much more severely compared with random or
sensing-based jamming. The deep learning classification scores are used by the
jammer for power control subject to an average power constraint. Next, a
generative adversarial network (GAN) is developed for the jammer to reduce the
time to collect the training dataset by augmenting it with synthetic samples.
As a defense scheme, the transmitter deliberately takes a small number of wrong
actions in spectrum access (in form of a causative attack against the jammer)
and therefore prevents the jammer from building a reliable classifier. The
transmitter systematically selects when to take wrong actions and adapts the
level of defense to mislead the jammer into making prediction errors and
consequently increase its throughput.

使用对抗机器学习对无线通信进行干扰攻击，通过深度学习分类器可靠预测下一个成功的传输并有效地干扰。开发了生成对抗网络以减少获取训练数据集的时间。作为一种防御方案，传输机故意采取一些错误的动作来干扰干扰器的可靠分类器，从而增加其吞吐量。