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.

中文翻译：

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DeepFake：基于深度决斗的欺骗策略来击败反应式干扰器


在本文中，我们介绍了 DeepFake，一种新颖的基于深度强化学习的欺骗策略，用于应对反应性干扰攻击。特别地，对于智能和反应性干扰攻击，如果干扰器检测到来自合法发射器的通信，则它能够感测信道并攻击信道。为了应对此类攻击，我们提出了一种智能欺骗策略，允许合法发射器发送“虚假”信号以吸引干扰器。然后，如果干扰器攻击信道，发射器可以利用强干扰信号通过使用环境反向散射通信技术来传输数据，或者从强干扰信号中收集能量以供将来使用。这样做不仅可以削弱干扰机的攻击能力，还可以利用干扰信号来提高系统性能。为了有效地学习和适应干扰攻击的动态性和不确定性，我们使用深度决斗神经网络架构开发了一种新颖的深度强化学习算法，其获得最优策略的速度比传统强化算法快数千倍。大量的仿真结果表明，我们提出的 DeepFake 框架在吞吐量、丢包率和学习率方面优于其他抗干扰策略。