Search

Cyber-Physical System consists of the integration of computational components in the cyber-domain with the physical-domain processes. In cyber-domain, embedded computers and networks monitor and control the physical processes, and in the physical-domain the sensors and actuators aid in interacting with the physical world. This interaction between the cyber and physical domain brings unique modeling challenges one of which includes the integration of discrete and sequential models in cyber-domain with the continuous and parallel physical domain processes. However, the same cyber-physical interaction also opens new opportunities for modeling. For example, the information flow in the cyber-domain manifests physically in the form of energy flows in the physical domain. Some of these energy flows unintentionally provide information about the cyber-domain through the side-channels. In this thesis, the first part consists of an extensive analysis of the side-channels (such as acoustic, magnetic, thermal, power and vibration) of the cyber-physical system is performed. Based on this analysis data-driven models are estimated. These models are then used to perform security vulnerability analysis (for confidentiality and integrity), whereby, new attack models are explored. Furthermore, the data-driven models are also utilized to create a defensive mechanism to minimize the information leakage from the system and to detect attacks to the integrity of the system. The cyber-physical manufacturing systems are taken as use cases to demonstrate the applicability of the modeling approaches. In the second part, side-channel analysis is performed to aid in modeling digital twins of the cyber-physical systems. Specifically, a novel methodology to utilize low-end sensors to analyze the side-channels and build the digital twins is proposed. These digital twins are used to capture the interaction between the cyber-domain and the physical domain of the manufacturing systems, and aid in process quality inference and fault localization. Using side-channels these digital twins are kept up-to-date, which is one of the fundamental requirements for building digital twins. Finally, challenges relating to performing data-driven modeling using non-Euclidean data in the cyber-physical system are addressed in the third part of the thesis. Moreover, a novel structural graph convolutional neural network and a dynamic graph embedding algorithm are presented to handle non-Euclidean data.

Cyber-physical systems consists of tight integration of cyber and physical domain components. Due to this, they are prone to various cross-domain attacks. One form of such attacks can take place in the form of physical-to-cyber domain attacks, which can cause confidentiality breach of the system. This is due to the fact that some of the cyber-domain information manifest in terms of physical actions such as motion, temperature change, etc. These physical actions may unintentionally leak information about the cyber-domain through the side-channels. Up until now there has been no study highlighting how these form of cross-domain attack can affect the cyber-physical additive manufacturing systems. Hence, in this thesis we present the analysis of acoustic side-channel to demonstrate how it can be leverage to build novel attack model and breach the confidentiality of the additive manufacturing system (such as 3D printers). Side-channels such as acoustic, thermal, and power allow attackers to acquire the information without actually leveraging the vulnerability of the algorithms implemented in the system. In 3D printers, geometry,process, and machine information are the intellectual properties, which are stored in the cyber domain (G-code). We have designed an attack model that consists of digital signal processing, machine learning algorithms, and context-based post processing to steal the intellectual property by reconstructing the G-code and thus the test objects. We have successfully reconstructed various test objects with an average axis prediction accuracy of 78.35% and an average length prediction error of 17.82%.