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ABSTRACT Industrial robotic systems in the era of Industry 4.0 play a pivotal role in modern manufacturing. These systems, which belong to the larger class of cyber‐physical systems (CPSs), rely heavily on advanced sensing capabilities to execute complex and delicate tasks with high precision and efficiency. It is of no surprise that the integration of sensors with Industry 4.0 robotic systems exposes them to potential cyber‐physical risks/threats. This paper addresses a critical gap in the literature of industrial robotics cybersecurity by presenting a comprehensive analysis of vulnerabilities in the sensing systems of industrial robots. In particular, we systematically explore how sensor performance limits, faults and biases can be exploited by attackers who can then turn these inherent weaknesses into security threats. Our investigation relies on a detailed literature review of a multitude of commonly used sensors in industrial robotic systems through the lens of their physics‐based operating principles, classifications, performance limits, potential faults and associated vulnerabilities against disturbances such as temperature fluctuations, electromagnetic and acoustic interference, and ambient light variations. The result of this systematic investigation is a ring chart illustrating the overlaps and entanglements of sensor faults and performance limits, which can be exploited by cyber‐physical adversaries. Additionally, we investigate the cascading effects of compromised sensor data on the operation of industrial robotic systems through a cause‐and‐effect analysis, where the sensor vulnerabilities can cause malfunction and lead to cyber‐physical damage. The result of this analysis is a sensor cyber‐physical threat cause‐and‐effect diagram, which can be employed for design of robust and effective cyber‐physical defence measures. By providing insights into sensor‐related cyber‐risks, our cyber‐physical threat analysis paves the path for enhanced industrial robotics security. 

The Controller Area Network (CAN) is widely used in the automotive industry for its ability to create inexpensive and fast networks. However, it lacks an authentication scheme, making vehicles vulnerable to spoofing attacks. Evidence shows that attackers can remotely control vehicles, posing serious risks to passengers and pedestrians. Several strategies have been proposed to ensure CAN data integrity by identifying senders based on physical layer characteristics, but high computational costs limit their practical use. This paper presents a framework to efficiently identify CAN bus system senders by fingerprinting them. By modeling the CAN sender identification problem as an image classification task, the need for expensive handcrafted feature engineering is eliminated, improving accuracy using deep neural networks. Experimental results show the proposed methodology achieves a maximum identification accuracy of 98.34%, surpassing the state-of-the-art method’s 97.13%. The approach also significantly reduces computational costs, cutting data processing time by a factor of 27, making it feasible for real-time application in vehicles. When tested on an actual vehicle, the proposed methodology achieved a no-attack detection rate of 97.78% and an attack detection rate of 100%, resulting in a combined accuracy of 98.89%. These results highlight the framework’s potential to enhance vehicle cybersecurity by reliably and efficiently identifying CAN bus senders. 

The Battery Management System (BMS) plays a crucial role in modern energy storage technologies, ensuring battery safety, performance, and longevity. However, as the BMS becomes more sophisticated and interconnected, it faces increasing cybersecurity challenges that can lead to catastrophic failures and safety hazards. This paper provides a comprehensive overview of cyberattacks targeting both traditional and wireless BMS. It explores various attack vectors, including malware injection, electromagnetic interference (EMI), temperature sensing manipulation, sensor malfunctioning and fault injection, and jamming attacks on modern BMS. Through threat modeling and vulnerability analysis, this paper examines the potential impacts on BMS functionality, safety, and performance. We highlight vulnerabilities associated with different BMS architectures and components, emphasizing the need for robust cybersecurity measures to protect against emerging threats. Cybersecurity measures are essential to protect the system from potential threats that could trigger false alarms, cause malfunctions, or lead to dangerous failures. Unauthorized access or tampering with the BMS can disrupt its fault response mechanisms, jeopardizing system performance and associated resources. Key cybersecurity strategies include intrusion detection systems (IDS), crypto-based authentication, secure firmware updates, and hardware-based security mechanisms such as trusted platform modules (TPMs). These measures strengthen BMS resilience by preventing unauthorized access and ensuring data integrity. Our findings are essential for mitigating risks in various sectors, including electric vehicles (EVs), renewable energy, and grid storage. They underscore the importance of ongoing research and development of adaptive security strategies to safeguard BMS against evolving cyber threats. Additionally, we propose a trust mechanism that secures the connection between input sensors and the BMS, ensuring the reliability and safety of battery-powered systems across various industries.