Deep Learning for Signal Authentication and Security in Massive Internet-of-Things Systems

Abstract

Secure signal authentication is arguably one of the most challenging problems in the Internet of Things (IoT), due to the large-scale nature of the system and its susceptibility to man-in-the-middle and data injection attacks. In this paper, a novel watermarking algorithm is proposed for dynamic authentication of IoT signals to detect cyber attacks. The proposed watermarking algorithm, based on a deep learning long shortterm 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 IoT gateway, which collects signals from the IoTDs, to effectively authenticate the reliability of the signals. Moreover, in massive IoT scenarios, since the gateway cannot authenticate all of the IoTDs simultaneously due to computational limitations, a game-theoretic framework is proposed to improve the gateway'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 the large set of available actions for the gateway, the MSNE is shown to be analytically challenging to derive, and, thus, a learning algorithm that converges to the MSNE is proposed. Moreover, in order to handle incomplete information scenarios in which the gateway 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 gateway 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 IoTDs with an almost 100% reliability. The results also show that, by optimally predicting the set of vulnerable IoTDs, the proposed deep reinforcement learning algorithm reduces the number of compromised IoTDs by up to 30%, compared to an equal probability baseline.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

“…However, this can be impractical since the IoT devices may not report their security status. Thus, the authors in [125] propose to use the DQL that enables the cloud to decide which IoT devices to authenticate with the incomplete information. Since IoT devices with more valuable data are likely to be attacked, the reward is defined as a function of data values of IoT devices.…”

Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks

“…Wireless security finds rich applications of deep learning. Deep learning was applied to authenticate signals [37], detect and classify jammers of different types [38], [39], [40], and control communications to mitigate jamming effects [41], [42], [47]. Using wireless sensors, deep learning was also used to infer private information in analogy to exploratory attacks [43].…”

IoT Network Security from the Perspective of Adversarial Deep Learning

“…In [18], machine learning was used to automatically identify the types of devices being connected to an IoT network and enable enforcement of rules for constraining the communications of vulnerable devices to minimize damage resulting from their compromise. In [19], deep learning was used to detect data injection and eavesdropping in IoT devices. In [20], machine learning was applied within an IoT gateway to detect anomalies in the data sent from the edge devices.…”

“…Recently, there has been a surge of efforts to apply machine learning to wireless security, including spoofing attacks [12], jamming attacks on data transmission [13], [14], and other attacks that target spectrum sensing [15] and signal classification [16] tasks. In particular, IoT system security benefits from machine learning to identify devices [17], [18], authenticate signals [19], and detect anomalies [20]. With growing applications of machine learning, it is necessary to understand the underlying security threats that target machine learning itself.…”