Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

TL;DR

This paper introduces a deep learning-based dynamic watermarking technique using LSTM to authenticate IoT signals, enhancing security against cyber attacks with low latency and high reliability.

Contribution

It presents a novel LSTM-based dynamic watermarking framework for IoT signals, improving attack detection and signal authentication in resource-constrained environments.

Findings

Achieves under 1 second attack detection delay

Provides nearly 100% message reliability

Effectively counters eavesdropping and data injection attacks

Abstract

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.