Cyber-physical Vulnerabilities Research Articles

Certificate-Based Signature Scheme for Industrial Internet of Things Using Hyperelliptic Curve Cryptography

The Industrial Internet of Things (IIoT) is a technology that uses the Internet of Things (IoT) infrastructure to sense, process, and communicate real-time events in the industrial system to cut down on unnecessary operating costs and to speed up industrial automation of internal and external working processes. Since the IIoT system inherits the same cyber-physical vulnerabilities that the IoT system already encounters, it requires additional work to address security concerns owing to its heterogeneous nature. As a result, an efficient security mechanism is essential to protect against various and unknown cyber-attacks. In this article, we propose a certificate-based signature scheme based on hyperelliptic curve cryptography (HECC), with the aim of improving security while reducing computational and communication costs in the IIoT environment. The proposed scheme outperforms existing schemes in terms of both computational and communication costs, as well as offering better security.

Verification of Numerically Controlled Manufacturing Processes, Toward Identifying Cyber-Physical Threats

Abstract Motivated by cyber-physical vulnerabilities in precision manufacturing processes, there is a need to externally examine the operational performance of computer numerically controlled (CNC) manufacturing systems. The overarching objective of this work is to design and fabricate a proof-of-concept CNC machine evaluation device, ultimately re-configurable to the mill, lathe, and 3D printing machine classes. This device will assist in identifying potential cyber-physical security threats in manufacturing systems by identifying perturbations outside the expected variations of machining processes and comparing the desired command inputted into the numerical controller and the actual machine performance (e.g., tool displacement, frequency). In this paper, a requirement-driven prototype device design method is presented and tested, and the results will be used to improve future iterations of the design. The first design iteration is tested on a Kuka KR 6 R700 series robotic arm, and machine movement comparisons are performed ex situ using Keyence laser measurement sensors. Data acquisition is performed with a Raspberry Pi 4 microcomputer, controlled by custom, cross-platform python code, and includes a touch screen human–computer interface (HCI). A device design adapted for a CNC mill is also presented, and the Haas TM-2 is used as a case study, which can be operated by CNC operators to assess machine performance, as needed, before a critical manufacturing process. This research will enable the broader goal of recognizing repeating advanced manufacturing performance deviations, which occur outside of a machine's normal variability, that could signal a malicious “attack.”

Cyber-physical vulnerabilities in additive manufacturing systems: A case study attack on the .STL file with human subjects

One of the key advantages of additive manufacturing (AM) is its digital thread, which allows for rapid communication, iteration, and sharing of a design model and its corresponding physical representation. While this enables a more efficient design process, it also presents opportunities for cyber-attacks to impact the physical word. In this paper the authors examine potential attack vectors along the Additive Manufacturing process chain. Specifically, the effects of cyber-physical attacks, and potential means for detecting them, are explored. To explore the potential implications of such an attack, a case study was conducted to evaluate the ability of human subjects to detect and diagnose a cyber-physical attack on the STL file of a test specimen. Based on the results of this study, recommendations are presented for preventing and detecting cyber-physical attacks on AM processes.

An approach to cyber-physical vulnerability assessment for intelligent manufacturing systems

The rampant increase in frequency and complexity of cyber-attacks against manufacturing firms has motivated the development of identification and assessment techniques for cyber-physical vulnerabilities in manufacturing. While the field of cybersecurity assessment approaches is expansive, there is a gap in assessments for cyber-physical vulnerabilities in intelligent manufacturing systems. In response, this paper provides an approach for systematically identifying cyber-physical vulnerabilities and analyzing their potential impact in intelligent manufacturing systems. The proposed approach employs intersection mapping to identify cyber-physical vulnerabilities in manufacturing. A cyber-physical vulnerability impact analysis using decision trees then provides the manufacturer with a stoplight scale between low, medium, and high levels of cyber-physical vulnerabilities for each production process. The stoplight scale allows manufacturers to interpret assessment results in an intuitive way. Finally, a case study of the proposed approach at an applied manufacturing research facility and general recommendations to securing similar facilities from cyber-physical attacks are provided.

Cyber-physical Vulnerability Assessment in Manufacturing Systems

The rampant increase in frequency and complexity of cyber-attacks against manufacturing firms, has motivated the development of identification and mitigation techniques for cyber-physical vulnerabilities in manufacturing. While the field of cybersecurity assessment approaches is expansive, there is no literature aimed at assessing cyber-physical vulnerabilities for manufacturing systems. In response, this paper provides a framework for systematically identifying cyber-physical vulnerabilities in manufacturing systems. The proposed approach employs intersection mapping to identify cyber-physical vulnerabilities in manufacturing. A cyber-physical vulnerability impact analysis using decision trees then provides the manufacturer with a stoplight scale between low, medium, and high levels of cyber-physical vulnerability for each analyzed production process. The stoplight scale allows manufacturers to interpret assessment results in an intuitive way. Finally, the paper provides a case study of the proposed approach at an applied manufacturing research facility and provides general recommendations to securing similar facilities from cyber-physical attacks.