  In this paper, we introduce DeepFake, a novel deep reinforcement
learning-based deception strategy to deal with reactive jamming attacks. In
particular, for a smart and reactive jamming attack, the jammer is able to
sense the channel and attack the channel if it detects communications from the
legitimate transmitter. To deal with such attacks, we propose an intelligent
deception strategy which allows the legitimate transmitter to transmit "fake"
signals to attract the jammer. Then, if the jammer attacks the channel, the
transmitter can leverage the strong jamming signals to transmit data by using
ambient backscatter communication technology or harvest energy from the strong
jamming signals for future use. By doing so, we can not only undermine the
attack ability of the jammer, but also utilize jamming signals to improve the
system performance. To effectively learn from and adapt to the dynamic and
uncertainty of jamming attacks, we develop a novel deep reinforcement learning
algorithm using the deep dueling neural network architecture to obtain the
optimal policy with thousand times faster than those of the conventional
reinforcement algorithms. Extensive simulation results reveal that our proposed
DeepFake framework is superior to other anti-jamming strategies in terms of
throughput, packet loss, and learning rate.
