Sujit Rokka Chhetri

Acoustic side-channel attacks on additive manufacturing systems

Additive manufacturing systems, such as 3D printers, emit sounds while creating objects. Our work demonstrates that these sounds carry process information that can be used to indirectly reconstruct the objects being printed, without requiring access to the original design. This is an example of a physical-to-cyber domain attack, where information gathered from the physical domain, such as acoustic side-channel, can be used to reveal information about the cyber domain. Our novel attack model consists of a pipeline of audio signal processing, machine learning algorithms, and context-based post-processing to improve the accuracy of the object reconstruction. In our experiments, we have successfully reconstructed the test objects (designed to test the attack model under various benchmark parameters) and their corresponding G-codes with an average accuracy for axis prediction of 78.35% and an average length prediction error of 17.82% on a Fused Deposition Modeling (FDM) based additive manufacturing system. Our work exposes a serious vulnerability in FDM based additive manufacturing systems exploitable by physical-to-cyber attacks that may lead to theft of Intellectual Property (IP) and trade secrets. To the best of our knowledge this kind of attack has not yet been explored in additive manufacturing systems.

KCAD: kinetic cyber-attack detection method for cyber-physical additive manufacturing systems

Additive Manufacturing (AM) uses Cyber-Physical Systems (CPS) (e.g., 3D Printers) that are vulnerable to kinetic cyber-attacks. Kinetic cyber-attacks cause physical damage to the system from the cyber domain. In AM, kinetic cyber-attacks are realized by introducing flaws in the design of the 3D objects. These flaws may eventually compromise the structural integrity of the printed objects. In CPS, researchers have designed various attack detection method to detect the attacks on the integrity of the system. However, in AM, attack detection method is in its infancy. Moreover, analog emissions (such as acoustics, electromagnetic emissions, etc.) from the side-channels of AM have not been fully considered as a parameter for attack detection. To aid the security research in AM, this paper presents a novel attack detection method that is able to detect zero-day kinetic cyber-attacks on AM by identifying anomalous analog emissions which arise as an outcome of the attack. This is achieved by statistically estimating functions that map the relation between the analog emissions and the corresponding cyber domain data (such as G-code) to model the behavior of the system. Our method has been tested to detect potential zero-day kinetic cyber-attacks in fused deposition modeling based AM. These attacks can physically manifest to change various parameters of the 3D object, such as speed, dimension, and movement axis. Accuracy, defined as the capability of our method to detect the range of variations introduced to these parameters as a result of kinetic cyber-attacks, is 77.45%.

Cross-domain security of cyber-physical systems

The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical systems, whereby we present a security analysis framework that can be used for generating novel cross-domain attack models, attack detection methods, etc. We demonstrate how information flows such as discrete domain signal flows and continuous domain energy flows in the cyber and physical domain can be used to generate model functions using data-driven estimation, and use this model functions for performing various cross-domain security analysis. We also demonstrate the practical applicability of the cross-domain security analysis framework using the cyber-physical manufacturing system as a case study.

Side Channels of Cyber-Physical Systems: Case Study in Additive Manufacturing

As 3-D printers are becoming increasingly relevant in various domains, including critical infrastructure, cyber-security questions naturally arise. This article investigates how to leverage analog emissions (vibration, acoustic, magnetic, and power) of 3-D printers in order to identify the printed object and compromise confidentiality. — Michail Maniatakos, New York University Abu Dhabi

Fix the leak! an information leakage aware secured cyber-physical manufacturing system

Cyber-physical additive manufacturing systems consists of tight integration of cyber and physical domains. This results in new cross-domain vulnerabilities that poses unique security challenges. One of the challenges is preventing confidentiality breach due to physical-to-cyber domain attacks, where attackers can analyze various analog emissions from the side-channels to steal the cyber-domain information. This information theft is based on the idea that an attacker can accurately estimate the relation between the analog emissions (acoustics, power, electromagnetic emissions, etc.,) and the cyber-domain data (such as G-code). To obstruct this estimation process, it is crucial to quantize the relation between the analog emissions and the cyber-data, and use it as a metric to generate computer aided manufacturing tools, such as slicing and tool-path generation algorithms, that are aware of these information leakage through the side-channels. In this paper, we present a novel methodology that uses mutual information as a metric to quantize the information leakage from the side-channels, and demonstrates how various design variables (such as object orientation, nozzle velocity, etc.,) can be used in an optimization algorithm to minimize the information leakage. Our methodology integrates this leakage aware algorithms to the state-of-the-art slicing and tool-path generation algorithms and achieves 24.76% average drop in the information leakage through acoustic side-channel. To the best of our knowledge, this is the first work that demonstrates the idea of generating information leakage aware computer aided manufacturing tools for protecting the confidentiality of the manufacturing system.

Confidentiality Breach Through Acoustic Side-Channel in Cyber-Physical Additive Manufacturing Systems

In cyber-physical systems, due to the tight integration of the computational, communication, and physical components, most of the information in the cyber-domain manifests in terms of physical actions (such as motion, temperature change, etc.). This leads to the system being prone to physical-to-cyber domain attacks that affect the confidentiality. Physical actions are governed by energy flows, which may be observed. Some of these observable energy flows unintentionally leak information about the cyber-domain and hence are known as the side-channels. 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. As a case study, we have taken cyber-physical additive manufacturing systems (fused deposition modeling-based three-dimensional (3D) printer) to demonstrate how the acoustic side-channel can be used to breach the confidentiality of 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 in the form of geometry details by reconstructing the G-code and thus the test objects. We have successfully reconstructed various test objects with an average axis prediction accuracy of 86% and an average length prediction error of 11.11%.

Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0

The next generation of smart manufacturing systems will incorporate various recent enabling technologies. These technologies will aid in ushering the era of the fourth industrial revolution. They will make the supply chain and the product lifecycle of the manufacturing system efficient, decentralized, and well-connected. However, these technologies have various security issues and, when integrated in the supply chain and the product lifecycle of manufacturing systems, can pose various challenges for maintaining the security requirements such as confidentiality, integrity, and availability. In this paper, we will present the various trends and advances in the security of the supply chain and product lifecycle of the manufacturing system while highlighting the roles played by the major enabling components of Industry 4.0.

Future Automation Engineering using Structural Graph Convolutional Neural Networks.

The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs. Classifying and clustering subgraphs according to their functionality is useful to discover functionally equivalent engineering artifacts that exhibit different graph structures. This paper presents a new graph learning algorithm designed to classify engineering data artifacts -- represented in the form of graphs -- according to their structure and neighborhood features. Our Structural Graph Convolutional Neural Network (SGCNN) is capable of learning graphs and subgraphs with a novel graph invariant convolution kernel and downsampling/pooling algorithm. On a realistic engineering-related dataset, we show that SGCNN is capable of achieving ~91% classification accuracy.

Poster Abstract: Thermal Side-Channel Forensics in Additive Manufacturing Systems

Additive manufacturing systems leak cyber-related information (such as G-code, M-code, etc.) from the side-channels (such as acoustic, power, thermal, etc.). In our work, we have successfully demonstrated the vulnerability of additive manufacturing to thermal side-channel attacks, where confidentiality can be breached to steal the Intellectual Property (IP) in the form of 3D design and printing parameters. We introduce a novel methodology to reverse engineer the thermal images acquired from the thermal side-channel to extract specific information (such as speed, temperature, axis of movement, etc.) present in the cyber-domain. To the best of our knowledge, this kind of forensics has not yet been explored in additive manufacturing systems.