We review the security requirements for vehicular communication networks and provide a critical assessment of some typical communication security solutions. We also propose a novel unconditionally secure vehicular communication architecture that utilizes the Kirchhoff-law-Johnson-noise (KLJN) key distribution scheme.
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating. In this work, we propose an efficient dual cyber-physical blockchain framework to build trust and secure communication for CV applications. Our approach incorporates blockchain technology and physical sensing capabilities of vehicles to quickly react to attacks in a large-scale vehicular network, with low resource overhead. We explore the application of our framework to three CV applications, i.e., highway merging, intelligent…
Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicio…
In a modern vehicle, there are over seventy Electronics Control Units (ECUs). For an in-vehicle network, ECUs communicate with each other by following a standard communication protocol, such as Controller Area Network (CAN). However, an attacker can easily access the in-vehicle network to compromise ECUs through a WLAN or Bluetooth. Though there are various deep learning (DL) methods suggested for securing in-vehicle networks, recent studies on adversarial examples have shown that attackers can easily fool DL models. In this research, we further explore adversarial examples in an in-vehicle ne…
The ubiquity of connecting technologies in smart vehicles and the incremental automation of its functionalities promise significant benefits, including a significant decline in congestion and road fatalities. However, increasing automation and connectedness broadens the attack surface and heightens the likelihood of a malicious entity successfully executing an attack. In this paper, we propose a Blockchain based Framework for sEcuring smaRt vehicLes (B-FERL). B-FERL uses permissioned blockchain technology to tailor information access to restricted entities in the connected vehicle ecosystem. I…
Electric Vehicles (EVs) can help alleviate our reliance on fossil fuels for transport and electricity systems. However, charging millions of EV batteries requires management to prevent overloading the electricity grid and minimise costly upgrades that are ultimately paid for by consumers. Managed chargers, such as Vehicle-to-Grid (V2G) chargers, allow control over the time, speed and direction of charging. Such control assists in balancing electricity supply and demand across a green electricity system and could reduce costs for consumers. Smart and V2G chargers connect EVs to the power grid u…
Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning
North Carolina State University（北卡罗来纳州立大学）；University of Louisville（路易斯维尔大学）；University of Maryland（马里兰大学）；University of North Texas（北德克萨斯大学）；Wayne State University（韦恩州立大学）
Microcontroller systems are integral to our daily lives, powering mission-critical applications such as vehicles, medical devices, and industrial control systems. Therefore, it is essential to investigate and outline the challenges encountered in developing secure microcontroller systems. While previous research has focused solely on microcontroller firmware analysis to identify and characterize vulnerabilities, our study uniquely leverages data from the 2023 and 2024 MITRE eCTF team submissions and post-competition interviews. This approach allows us to dissect the entire lifecycle of secure …
The transportation system is rapidly evolving with new connected and automated vehicle (CAV) technologies that integrate CAVs with other vehicles and roadside infrastructure in a cyberphysical system (CPS). Through connectivity, CAVs affect their environments and vice versa, increasing the size of the cyberattack surface and the risk of exploitation of security vulnerabilities by malicious actors. Thus, greater understanding of potential CAV-CPS cyberattacks and of ways to prevent them is a high priority. In this article we describe CAV-CPS cyberattack surfaces and security vulnerabilities, an…
Mississippi State University（密西西比州立大学）；University of Tennessee at Martin（田纳西大学马丁分校）
The unmanned aerial vehicle (UAV) is one of the technological breakthroughs that supports a variety of services, including communications. UAV will play a critical role in enhancing the physical layer security of wireless networks. This paper defines the problem of eavesdropping on the link between the ground user and the UAV, which serves as an aerial base station (ABS). The reinforcement learning algorithms Q-learning and deep Q-network (DQN) are proposed for optimizing the position of the ABS and the transmission power to enhance the data rate of the ground user. This increases the secrecy …
MAVSec: Securing the MAVLink Protocol for Ardupilot/PX4 Unmanned Aerial Systems
Taif University（塔伊夫大学）；University of Sfax（斯法克斯大学）；Prince Sultan University（苏丹王子大学）；ISEP（高等工程学院）；Gaitech Robotics（Gaitech机器人公司）；Jinan University（暨南大学）；University of Carthage（迦太基大学）
Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detec…
TNO（荷兰应用科学研究组织）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）
Multiple attacks have shown that in-vehicle networks have vulnerabilities which can be exploited. Securing the Controller Area Network (CAN) for modern vehicles has become a necessary task for car manufacturers. Some attacks inject potentially large amount of fake messages into the CAN network; however, such attacks are relatively easy to detect. In more sophisticated attacks, the original messages are modified, making the detection a more complex problem. In this paper, we present a novel machine learning based intrusion detection method for CAN networks. We focus on detecting message modific…
AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons
Networked Systems Security (NSS) Group\ Royal Institute of Technology Stockholm Sweden；Networked Systems Security (NSS) Group\ Royal Institute of Technology
CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation
Laboratory of Cryptography and System Security (CrySyS Lab)（密码与系统安全实验室（CrySyS实验室））；Budapest University of Technology and Economics（布达佩斯技术大学）；HUN-REN-BME Information Systems Research Group（匈牙利-人-布达佩斯信息系统研究组）
