Cyber-physical systems (CPS) often rely on external communication for
supervisory control or sensing. Unfortunately, these communications render the
system vulnerable to cyber-attacks. Attacks that alter messages, such as replay
attacks that record measurement signals and then play them back to the system,
can cause devastating effects. Dynamic Watermarking methods, which inject a
private excitation into control inputs to secure resulting measurement signals,
have begun addressing the challenges of detecting these attacks, but have been
restricted to linear time invariant (LTI) systems. Though LTI models are
sufficient for some applications, other CPS, such as autonomous vehicles,
require more complex models. This paper develops a linear time-varying (LTV)
extension to previous Dynamic Watermarking methods by designing a matrix
normalization factor to accommodate the temporal changes in the system.
Implementable tests are provided with considerations for real-world systems.
The proposed method is then shown to be able to detect generalized replay
attacks both in theory and in simulation using a LTV vehicle model.

通过设计矩阵归一化因子来扩展动态数字水印方法以适应线性时变系统，本文提出的方法在理论和仿真中展示了能够检测线性时变车辆模型中的广义回放攻击。

通过时间变动动态水印检测通用重放攻击

Detecting Generalized Replay Attacks via Time-Varying Dynamic  Watermarking

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.

本文提出了一种基于深度学习的动态数码水印方法以检测物联网 (IoT) 信号的网络攻击，该方法利用长短期记忆 (LSTM) 结构从信号中提取一组随机特征并将这些特征动态水印到信号中，可以保障 IoT 设备信号的可靠性，并有效地防止数据注入、窃听和中间人攻击等网络攻击。模拟结果表明，在攻击检测延迟小于 1 秒的情况下，消息的传输可以达到近乎 100% 的可靠性。