Yuzhe Li

Bio: Yuzhe Li is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Computer science & Estimator. The author has an hindex of 15, co-authored 40 publications receiving 1128 citations. Previous affiliations of Yuzhe Li include Hong Kong University of Science and Technology & University of Alberta.

Jamming Attacks on Remote State Estimation in Cyber-Physical Systems: A Game-Theoretic Approach

Abstract: We consider security issues in remote state estimation of Cyber-Physical Systems (CPS). A sensor node communicates with a remote estimator through a wireless channel which may be jammed by an external attacker. With energy constraints for both the sensor and the attacker, the interactive decision making process of when to send and when to attack is studied. We formulate a game-theoretic framework and prove that the optimal strategies for both sides constitute a Nash equilibrium of a zero-sum game. To tackle the computation complexity issues, we present a constraint-relaxed problem and provide corresponding solutions using Markov chain theory.

SINR-Based DoS Attack on Remote State Estimation: A Game-Theoretic Approach

Abstract: We consider remote state estimation of cyberphysical systems under signal-to-interference-plus-noise ratio-based denial-of-service attacks. A sensor sends its local estimate to a remote estimator through a wireless network that may suffer interference from an attacker. Both the sensor and the attacker have energy constraints. We first study an associated two-player game when multiple power levels are available. Then, we build a Markov game framework to model the interactive decision-making process based on the current state and information collected from previous time steps. To solve the associated optimality (Bellman) equations, a modified Nash Q-learning algorithm is applied to obtain the optimal solutions. Numerical examples and simulations are provided to demonstrate our results.

Detection Against Linear Deception Attacks on Multi-Sensor Remote State Estimation

Abstract: In this paper, a security problem in cyberphysical systems (CPS) is studied. A remote state estimation process using multiple sensors is considered. The measurement innovation packets from each sensor, which may be modified by a malicious attacker, are sent to a remote fusion center through wireless communication channels. To avoid being detected by typical bad data detectors at the remote estimator's side, the attacker would maintain the statistical properties of the measurements. Based on the information extracted from the trusted sensors and the correlations between the trusted sensors and the suspicious sensors, we propose three sequential data verification and fusion procedures for different detection information scenarios. The corresponding impacts of possible attacking patterns on the estimation performance under different detectors are analyzed explicitly. Simulations are provided to illustrate the developed results.

False Data Injection Attacks on Networked Control Systems: A Stackelberg Game Analysis

Abstract: In this paper, a security problem in networked control systems (NCS) is studied. In a standard linear quadratic Gaussian (LQG) control scenario in NCS, a so-called false data injection attack could be launched by a malicious attacker to deteriorate the system performance without being detected. To defend against such attacks, a defender on the NCS side needs to allocate defense resources among the sensors to secure the data, and the defense investment determines the costs of compromising certain sensors. After observing the defender’ action, the attacker decides the target sensors to compromise. While both sides are subject to the resource constraints, the interactive decision-making between the defender and the attacker is investigated in a Stackelberg game (leader–follower game) framework. The optimal solutions for both sides under different types of budget constraints are analyzed. Simulation examples are provided to illustrate the main results.

IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?

Abstract: The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.

Machine Learning in IoT Security: Current Solutions and Future Challenges

Abstract: The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective

Abstract: The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the IoT to enable the interconnection of anything, anywhere, and at any time in the manufacturing system context to improve the productivity, efficiency, safety, and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality-of-service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs and to present a unified assessment of the technology from a systems’ perspective, in this paper, we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation and process automation), and their characteristics. We then consider existing research efforts from the three key system aspects of control, networking, and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine communication, and software-defined networking. Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning in an I-IoT context.

Jamming Attacks on Remote State Estimation in Cyber-Physical Systems: A Game-Theoretic Approach

Abstract: We consider security issues in remote state estimation of Cyber-Physical Systems (CPS). A sensor node communicates with a remote estimator through a wireless channel which may be jammed by an external attacker. With energy constraints for both the sensor and the attacker, the interactive decision making process of when to send and when to attack is studied. We formulate a game-theoretic framework and prove that the optimal strategies for both sides constitute a Nash equilibrium of a zero-sum game. To tackle the computation complexity issues, we present a constraint-relaxed problem and provide corresponding solutions using Markov chain theory.