Driven by advances in information and communication technologies, an increasing number of industries embrace unmanned and autonomous vehicles for services, such as public transportation, shipping, mapping, and remote surveillance. Unfortunately, these vehicles are vulnerable to passive and active cyber‐physical attacks that can be used for industrial espionage and hijacking attempts. Since attackers can use hijacked vehicles as weapons in terrorist attacks, ensuring the secure operation of such vehicles is critical to prevent the attacks from causing dire financial consequences, or worse, the loss of human lives. This study is motivated by the observation that most cybersecurity studies provide superficial, high‐level descriptions of vulnerabilities and attacks, and the true impact of the described attacks remains unclear. To address this problem, we demonstrate advanced manipulation attacks against an underactuated Unmanned Surface Vehicle (USV) which results in successful hijackings. Using state‐of‐the‐art cryptography, we also show how the signal transmission can be secured to avoid hijacking attempts actively steering the vehicle off course. Through field experiments, we demonstrate how the attacks affect the closed‐loop guidance, navigation, and control system and how the proposed countermeasures prevent these attacks from being successful. Our study is unique in that we provide a complete description of the attacked USV and give a detailed analysis of how spoofed navigation estimates affect the closed‐loop behavior of the underactuated USV.

This paper surveys security threats to 5G-enabled wireless access networks for social robots in public spaces (SRPS). The use of social robots (SR) in public areas requires specific Quality of Service (QoS) planning to meet its unique requirements. Its 5G threat landscape entails more than cybersecurity threats that most previous studies focus on. This study examines the 5G wireless RAN for SRPS from three perspectives: SR and wireless access points, the ad hoc network link between SR and user devices, and threats to SR and users’ communication equipment. The paper analyses the security threats to confidentiality, integrity, availability, authentication, authorisation, and privacy from the SRPS security objectives perspective. We begin with an overview of SRPS use cases and access network requirements, followed by 5G security standards, requirements, and the need for a more representative threat landscape for SRPS. The findings confirm that the RAN of SRPS is most vulnerable to physical, side-channel, intrusion, injection, manipulation, and natural and malicious threats. The paper presents existing mitigation to the identified attacks and recommends including physical level security (PLS) and post-quantum cryptography in the early design of SRPS. The insights from this survey will provide valuable risk assessment and management input to researchers, industrial practitioners, policymakers, and other stakeholders of SRPS.

Ensuring physical-layer security (PLS) in wireless communication has always been a challenge due to the broadcasting nature of the transmission. In this work, a multilevel chaotic encryption (MCE) system is proposed to improve PLS in Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems under Rayleigh fading channel. Additionally, the proposed technique improves the transmission performance by reducing the high Peak-to-Average-Power Ratio (PAPR) which is also considered as one of the major drawbacks of Multicarrier Modulations (MCM) like OFDM. In the proposed scheme, multilevel encryption is achieved in two steps. In the first step, a precoding matrix based on a unique chaotic sequence scrambles the modulated symbols. In the second step, phase scrambling is used based on chaotic sequence for selective mapping to provide a second level of encryption for the entire transmission. We evaluated the security performances using randomness of the proposed MCE, achievable key space, and security analysis under Cryptographic attacks from the perspective of Cryptanalysis. We verified the communication performances in terms of computational complexity of the system, PAPR, and error performances considering multipath Rayleigh fading wireless channel. The proposed MCE provides a huge key space of ~10302 which can resist any cryptographic attacks. Also, the pseudo-random nature of the multilevel chaotic scrambling reduces the PAPR by reducing autocorrelation of the OFDM signal. The proposed low complexity MCE solution outperforms existing encryption schemes when compared in terms of security, computational complexity, and PAPR. Mathematical analysis and simulation results show that the proposed MCE scheme can enhance physical layer security along with significant reduction in PAPR without compromising the error performances of the system.

An ever-increasing number of autonomous vehicles use bandwidth-greedy sensors such as cameras and LiDARs to sense and act to the world around us. Unfortunately, signal transmission in vehicles is vulnerable to passive and active cyber-physical attacks that may result in loss of intellectual property, or worse yet, the loss of control of a vehicle, potentially causing great harm. Therefore, it is important to investigate efficient cryptographic methods to secure signal transmission in such vehicles against outside threats. This study is motivated by the observation that previous publications have suggested legacy algorithms, which are either inefficient or insecure for vision-based signals. We show how stream ciphers and authenticated encryption can be applied to transfer sensor data securely and efficiently between computing devices suitable for distributed guidance, navigation, and control systems. We provide an efficient and flexible pipeline of cryptographic operations on image and point cloud data in the Robot Operating System (ROS). We also demonstrate how image data can be compressed to reduce the amount of data to be encrypted, transmitted, and decrypted. Experiments on embedded computers verify that modern software cryptographic algorithms perform very well on large sensor data. Hence, the introduction of such algorithms should enhance security without significantly compromising the overall performance.

We consider encrypted guidance systems for straight-line path following. With cloud-computing technology, we can outsource computations in guidance systems to third-party providers, increasing scalability and enabling guidance-as-a-service. However, by remotely hosting a conventional guidance system on third-party infrastructure, we leak information such as the path of the vehicle. Potential customers may consider these leaks a serious breach of confidentiality, which limits the practical use of such systems. Therefore, to make cloud-based guidance systems viable, we would like to design guidance systems that we can host remotely without revealing confidential information to the host. To this end, we show that we can construct guidance systems that operate on encrypted position measurements, encrypted waypoints, and the bearing between each waypoint by using homomorphic encryption, effectively preventing the cloud host from identifying the vehicle's position. We show that the proposed guidance laws are locally exponentially stable and that the induced computational latency is appropriate for the real-time guidance of autonomous vehicles. Through field experiments, we demonstrate that an encrypted guidance system is practical and allows an unmanned surface vehicle to follow an encrypted path. The originality of this work lies in conceptualizing, designing, and experimentally validating an encrypted guidance system, unlike other studies that considered encrypted control systems.