The Controller Area Network (CAN) is a bus protocol widely used in Electronic control Units (ECUs) to communicate between various subsystems in vehicles. Insecure CAN networks can allow attackers to control information between vital vehicular subsystems. As vehicles can have lifespans of multiple decades, post-quantum cryptosystems are essential for protecting the vehicle communication systems from quantum attacks. However, standard CAN’s efficiency and payload sizes are too small for post-quantum cryptography. The Controller Area Network Flexible Data-Rate (CAN-FD) is an updated protocol for CAN that increases transmission speeds and maximum payload size. With CAN-FD, higher security standards, such as post-quantum, can be utilized without severely impacting performance. In this paper, we propose PUF-Based Post-Quantum Cryptographic CAN-FD Framework, or PUF-PQC-CANFD. Our framework provides post-quantum security to the CAN network while transmitting and storing less information than other existing pre-quantum and post-quantum CAN frameworks. Our proposal protects against most cryptographic-based attacks while transmitting (at up to 100 ECUs) 25–94% less messages than existing pre-quantum frameworks and 99% less messages than existing post-quantum frameworks. PUF-PQC-CANFD is optimized for smaller post-quantum key sizes, storage requirements, and transmitted information to minimize the impact on resource-restricted ECUs.

As the popularity of 3D printing or additive manufacturing (AM) continues to increase for use in commercial and defense supply chains, the requirement for reliable, robust protection from adversaries has become more important than ever. Three-dimensional printing security focuses on protecting both the individual Industrial Internet of Things (I-IoT) AM devices and the networks that connect hundreds of these machines together. Additionally, rapid improvements in quantum computing demonstrate a vital need for robust security in a post-quantum future for critical AM manufacturing, especially for applications in, for example, the medical and defense industries. In this paper, we discuss the attack surface of adversarial data manipulation on the physical inter-device communication bus, Controller Area Network (CAN). We propose a novel, hierarchical tree solution for a secure, post-quantum-supported security framework for CAN-based AM devices. Through using subnet hopping between isolated CAN buses, our framework maintains the ability to use legacy or third-party devices in a plug-and-play fashion while securing and minimizing the attack surface of hardware Trojans or other adversaries. The results of the physical implementation of our framework demonstrate 25% and 90% improvement in message costs for authentication compared to existing lightweight and post-quantum CAN security solutions, respectively. Additionally, we performed timing benchmarks on the normal communication (hopping) and authentication schemes of our framework.

Recent advancements in 3D-printing/additive manufacturing has brought forth a new interest in the use of controller area network (CAN) for multimodule, plug-and-play bus support for their embedded systems. CAN systems provide a variety of benefits that can outweigh typical conventional wire-loom protocols in many categories. However, implementation of CAN also brings forth vulnerabilities provided by its spoofable, destination-encoded shared communication bus. These vulnerabilities result in undetectable fault injection, packet manipulation, unauthorized packet logging/sniffing, and more. They also provide attackers the capability to manipulate all sensor information, commands, and create unsafe operating conditions using only a single compromised node on the CAN network (bypassing all root-of-trust in the modules). Thus, malicious hardware requires only a connection to the bus for access to all traffic. In this article, we discuss the effects of repurposed CAN-based attacks capable of manipulating sensor data, overriding systems, and injecting dangerous commands on the CAN using various entry methods. As a case study, we also showed a spoofing attack on critical data modules within a commercial 3D printer.

Additive Manufacturing (AM) is gaining renewed popularity and attention due to low-cost fabrication systems proliferating the market. Current communication protocols used in AM limit the connection flexibility between the control board and peripherals; they are often complex in their wiring and thus restrict their avenue of expansion. Thus, the Controller Area Network (CAN) bus is an attractive pathway for inter-hardware connections due to its innate quality. However, the combination of CAN and AM is not well explored and documented in existing literature. This article aims to provide examples of CAN bus applications in AM.

Cyber-physical control systems are critical infrastructures designed around highly responsive feedback loops that are measured and manipulated by hundreds of sensors and controllers. Anomalous data, such as from cyber-attacks, greatly risk the safety of the infrastructure and human operators. With recent advances in the quantum computing paradigm, the application of quantum in anomaly detection can greatly improve identification of cyber-attacks in physical sensor data. In this paper, we explore the use of strong pre-processing methods and a quantum-hybrid Support Vector Machine (SVM) that takes advantage of fidelity in parameterized quantum circuits to efficiently and effectively flatten extremely high dimensional data. Our results show an F-1 Score of 0.86 and accuracy of 87% on the HAI CPS dataset using an 8-qubit, 16-feature quantum kernel, performing equally to existing work and 14% better than its classical counterpart.

Sensitive data captured by Industrial Control Systems (ICS) play a large role in the safety and integrity of many critical infrastructures. Detection of anomalous or malicious data, or Anomaly Detection (AD), with machine learning is one of many vital components of cyberphysical security. Quantum kernel-based machine learning methods have shown promise in identifying complex anomalous behavior by leveraging the highly expressive and efficient feature spaces of quantum computing. This study focuses on the parameterization of Quantum Hybrid Support Vector Machines (QSVMs) using three popular datasets from Cyber-Physical Systems (CPS). The results demonstrate that QSVMs outperform traditional classical kernel methods, achieving 13.3% higher F1 scores. Additionally, this research investigates noise using simulations based on real IBMQ hardware, revealing a maximum error of only 0.98% in the QSVM kernels. This error results in an average reduction of 1.57% in classification metrics. Furthermore, the study found that QSVMs show a 91.023% improvement in kernel-target alignment compared to classical methods, indicating a potential "quantum advantage" in anomaly detection for critical infrastructures. This effort suggests that QSVMs can provide a substantial advantage in anomaly detection for ICS, ultimately enhancing the security and integrity of critical infrastructures.