  Secure signal authentication is arguably one of the most challenging problems
in the Internet of Things (IoT) environment, due to the large-scale nature of
the system and its susceptibility to man-in-the-middle and eavesdropping
attacks. In this paper, a novel deep learning method is proposed for dynamic
authentication of IoT signals to detect cyber attacks. The proposed learning
framework, based on a long short-term memory (LSTM) structure, enables the IoT
devices (IoTDs) to extract a set of stochastic features from their generated
signal and dynamically watermark these features into the signal. This method
enables the cloud, which collects signals from the IoT devices, to effectively
authenticate the reliability of the signals. Moreover, in massive IoT
scenarios, since the cloud cannot authenticate all the IoTDs simultaneously due
to computational limitations, a game-theoretic framework is proposed to improve
the cloud's decision making process by predicting vulnerable IoTDs. The
mixed-strategy Nash equilibrium (MSNE) for this game is derived and the
uniqueness of the expected utility at the equilibrium is proven. In the massive
IoT system, due to a large set of available actions for the cloud, it is shown
that analytically deriving the MSNE is challenging and, thus, a learning
algorithm proposed that converges to the MSNE. Moreover, in order to cope with
the incomplete information case in which the cloud cannot access the state of
the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed
to dynamically predict the state of unauthenticated IoTDs and allow the cloud
to decide on which IoTDs to authenticate. Simulation results show that, with an
attack detection delay of under 1 second the messages can be transmitted from
IoT devices with an almost 100% reliability.
