Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things
This addresses security challenges for IoT systems, which require low-latency and computationally efficient solutions, but it appears incremental as it builds on existing watermarking and LSTM techniques.
The paper tackles the problem of securing IoT devices against cyber attacks like data injection and eavesdropping by proposing a deep learning-based dynamic watermarking method, achieving nearly 100% reliability in message transmission with an attack detection delay under 1 second.
Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. 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.