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The automotive industry is set for a rapid transformation in the next few years in terms of communication. The kind of growth the automotive industry is poised for in fields of connected cars is both fascinating and alarming at the same time. The communication devices equipped to the cars and the data exchanges done between vehicles to vehicles are prone to a lot of cyber-related attacks. The signals that are sent using Vehicular Adhoc Network (VANET) between vehicles can be eavesdropped by the attackers and it may be used for various attacks such as the man in the middle attack, DOS attack, Sybil attack, etc. These attacks can be prevented using the Blockchain technology, where each transaction is logged in a decentralized immutable Blockchain ledger. This provides authenticity and integrity to the signals. But the use of Blockchain Platforms such as Ethereum has various drawbacks like scalability which makes it infeasible for connected car system.

With the rapid development of vehicle intelligent and networking technology, the IT security of automotive systems becomes an important area of research. In addition to the basic vehicle control, intelligent advanced driver assistance systems, infotainment systems will all exchange data with in-vehicle network. Unfortunately, current communication network protocols, including Controller Area Network (CAN), FlexRay, MOST, and LIN have no security services, such as authentication or encryption, etc. Therefore, the vehicle are unprotected against malicious attacks. Since CAN bus is actually the most widely used field bus for in-vehicle communications in current automobiles, the security aspects of CAN bus is focused on. Based on the analysis of the current research status of CAN bus network security, this paper summarizes the CAN bus potential security vulnerabilities and the attack means.

This paper is the second in the series of documents designed to record the progress of a series of SAE documents - SAE J2836™, J2847, J2931, & J2953 - within the Plug-In Electric Vehicle (PEV) Communication Task Force. This follows the initial paper number 2010-01-0837, and continues with the test and modeling of the various PLC types for utility programs described in J2836/1™ & J2847/1. This also extends the communication to an off-board charger, described in J2836/2™ & J2847/2 and includes reverse energy flow described in J2836/3™ and J2847/3. The initial versions of J2836/1™ and J2847/1 were published early 2010. J2847/1 has now been re-opened to include updates from comments from the National Institute of Standards Technology (NIST) Smart Grid Interoperability Panel (SGIP), Smart Grid Architectural Committee (SGAC) and Cyber Security Working Group committee (SCWG).

Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality.

In the past, research on blockchain technology has addressed security and privacy concerns within intelligent transportation systems for critical V2I and V2V communications that form the backbone of Internet of Vehicles. Within trucking industry, a recent trend has been observed towards the use of blockchain technology for operations. Industry stakeholders are particularly looking forward to refining status quo contract management and vehicle maintenance processes through blockchains. However, the use of blockchain technology for enhancing vehicle performance in fleets, especially while considering the fact that modern-day intelligent vehicles are prone to cyber security threats, is an area that has attracted less attention. In this paper, we demonstrate a case study that makes use of blockchains to securely optimize the fuel economy of fleets that do package pickup and delivery (P&D) in urban areas.

As a promising strategic industry group that is rapidly evolving around the world, autonomous vehicle is entering a critical phase of commercialization from demonstration to end markets. The global automotive industry and governments are facing new common topics and challenges brought by autonomous vehicle, such as how to test, assess, and administrate the autonomous vehicle to ensure their safe running in real traffic situations and proper interactions with other road users. Starting from the facts that the way to autonomous driving is the process of a robot or a machine taking over driving tasks from a human. This paper summarizes the main characteristics of autonomous vehicle which are different from traditional one, then demonstrates the limitations of the existing certification mechanism and related testing methods when applied to autonomous vehicle.

Recent developments in the commercialization of mobility services have brought unprecedented connectivity to the automotive sector. While the adoption of connected features provides significant benefits to vehicle owners, adversaries may leverage zero-day attacks to target the expanded attack surface and make unauthorized access to sensitive data. Protecting new generations of automotive controllers against malicious intrusions requires solutions that do not depend on conventional countermeasures, which often fall short when pitted against sophisticated exploitation attempts. In this paper, we describe some of the latent risks in current automotive systems along with a well-engineered multi-layer defense strategy. Further, we introduce a novel and comprehensive attack and performance test framework which considers state-of-the-art memory corruption attacks, countermeasures and evaluation methods.

In recent years, with increase in external connectivity (V2X, telematics, mobile projection, BYOD) the automobile is becoming a target of cyberattacks and intrusions. Any such intrusion reduces customer trust in connected cars and negatively impacts brand image (like the recent Jeep Cherokee hack). To protect against intrusion, several mechanisms are available. These range from a simple secure CAN to a specialized symbiote defense software. A few systems (e.g. V2X) implement detection of an intrusion (defined as a misbehaving entity). However, most of the mechanisms require a system-wide change which adds to the cost and negatively impacts the performance. In this paper, we are proposing a practical and scalable approach to intrusion detection. Some benefits of our approach include use of existing security mechanisms such as TrustZone® and watermarking with little or no impact on cost and performance. In addition, our approach is scalable and does not require any system-wide changes.

Automotive safety is always the focus of consumers, the selling point of products, the focus of technology. In order to achieve automatic driving, interconnection with the outside world, human-automatic system interaction, the security connotation of intelligent and connected vehicles (ICV) changes: information security is the basis of its security. Functional safety ensures that the system is operating properly. Behavioral safety guarantees a secure interaction between people and vehicles. Passive security should not be weakened, but should be strengthened based on new constraints. In terms of information safety, the threshold for attacking cloud, pipe, and vehicle information should be raised to ensure that ICV system does not fail due to malicious attacks. The cloud is divided into three cloud platforms according to functions: ICVs private cloud, TSP cloud, public cloud.

Significant growth of Unmanned Aerial Vehicles (UAV) has unlocked many services and applications opportunities in the healthcare sector. Aerial transportation of medical cargo delivery can be an effective and alternative way to ground-based transport systems in times of emergency. To improve the security and the trust of such aerial transportation systems, Blockchain can be used as a potential technology to manage, operate and monitor the entire process. In this paper, we present a blockchain network solution based on Ethereum for the transportation of medical cargo such as blood, medicines, vaccines, etc. The smart contract solution developed in solidity language was tested using the Truffle program. Ganache blockchain test network was employed to host the blockchain network and test the operation of the proposed blockchain model. The suitability of the model is validated in real-time using a UAV and all the flight data are captured and uploaded into the blockchain.

With the rapid development of connected and autonomous vehicles, more sophisticated automotive systems running large portions of software and implementing a variety of communication interfaces are being developed. The ever-expanding codebase increases the risk for software vulnerabilities, while at the same time the large number of communication interfaces make the systems more susceptible to be targeted by attackers. As such, it is of utmost importance for automotive organizations to identify potential vulnerabilities early and continuously in the development lifecycle in an automated manner. In this paper, we suggest a practical approach for integrating fuzz testing into a Continuous Integration (CI) pipeline for automotive systems. As a first step, we have performed a Threat Analysis and Risk Assessment (TARA) of a general E/E architecture to identify high-risk interfaces and functions.

Vehicles have more connectivity options now-a-days and these increasing connection options are giving more chances for an intruder to exploit the system. So, the vehicle manufacturers need to make the ECU in the vehicle more secure. To make the system secure, the embedded system must secure all the assets in the system. Examples of assets are Software, Kernel or Operating system, cryptographic Keys, Passwords, user data, etc. In this, securing the Kernel is extremely important as an intruder can even exploit the operating system characteristics just by changing the kernel code without introducing a trojan in the system. Also, the Kernel is the one entity that manages all permissions, so, if the kernel is hacked, these permissions also get compromised. The proposed approach is to make the kernel secure by doing the integrity check periodically of the kernel code loaded into the main memory of the system.

Access control enforces security policies for controlling critical resources. For V2X (Vehicle to Everything) autonomous military vehicle fleets, network middleware systems such as ROS (Robotic Operating System) expose system resources through networked publisher/subscriber and client/server paradigms. Without proper access control, these systems are vulnerable to attacks from compromised network nodes, which may perform data poisoning attacks, flood packets on a network, or attempt to gain lateral control of other resources. Access control for robotic middleware systems has been investigated in both ROS1 and ROS2. Still, these implementations do not have mechanisms for evaluating a policy's consistency and completeness or writing expressive policies for distributed fleets. We explore an RBAC (Role-Based Access Control) mechanism layered onto ROS environments that uses local permission caches with precomputed truth tables for fast policy evaluation.

In agriculture industry, increasing use of Vehicle Internet of Things (IoT), telematics and emerging technologies are resulting in smarter machines with connected solutions. Inter and Intra Communication with vehicle to vehicle and inside vehicle - Electronic Control Unit (ECU) to ECU or ECU (Electronic Control Unit) to sensor, requirement for flow of data increased in-turn resulting in increased need for secure communication. In this paper, we focus on functional verification and validation of secure Controller Area Network (CAN) for intra vehicular communication to establish confidentiality, integrity, authenticity, and freshness of data, supporting safety, advanced automation, protection of sensitive data and IP (Intellectual Property) protection. Network security algorithms and software security processes are the layers supporting to achieve our cause.

Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.

Upon their arrival, Unmanned Autonomous Systems (UAS) brought with them many benefits for those involved in a military campaign. They can use such systems to reconnoiter dangerous areas, provide 24-hr aerial security surveillance for force protection purposes or even attack enemy targets all the while avoiding friendly human losses in the process. Unfortunately, these platforms also carry the inherent risk of being built on innately vulnerable cybernetic systems. From software which can be tampered with to either steal data, damage or even outright steal the aircraft, to the data networks used for communications which can be jammed or even eavesdropped on to gain access to sensible information. All this has the potential to turn the benefits of UAS into liabilities and although the last decade has seen great advances in the development of protection and countermeasures against the described threats and beyond the risk still endures.

Autonomous driving represents the ultimate goal of future automobile development. As a collaborative application that integrates vehicles, road infrastructure, network and cloud, autonomous driving business requires a high-degree dynamic cooperation among multiple resources such as data, computing and communications that are distributed throughout the system. In order to meet the anticipated high demand for resources and performance requirements of autonomous driving, and to ensure the safety and comfort of the vehicle users and pedestrians, a top concern of autonomous driving is to understand the system requirements for resources and conduct an in-depth analysis of the autonomous driving business. In this context, this paper presents a comprehensive analysis of the typical business for autonomous driving and establishes an analysis model for five common capabilities, i.e. collection, transmission, intelligent computing, human-machine interaction (HMI), and security.

Software vulnerability management is one of the most critical and crucial security techniques, which analyzes the automotive software/firmware across the digital cockpit, ADAS, V2X, etc. domains for vulnerabilities, and provides security patches for the concerned Common Vulnerabilities and Exposures (CVE). The process of automotive SW/FW vulnerability management system between the OEMs and vendors happen through a channel of fixing a certain number of vulnerabilities by 1st tier supplier which needs to be verified in front of OEMs for the fixed number and type of patches in there deliverable SW/FW. The gap of verification between for the fixed patches between the OEMs and 1st tier supplier requires a reliable human independent intelligent technique to have a trustworthiness of verification.

Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used?

In the past, aircraft network design did not demand for information security considerations. The aircraft systems were simple, obscure, proprietary and, most importantly for security, the systems have been either physically isolated or they have been connected by directed communication links. The union of the aircraft systems thus formed a federated network. These properties are in sharp contrast with today’s system designs, which rest upon platform-based solutions with shared resources being interconnected by a massively meshed and shared communication network. The resulting connectivity and the high number of interfaces require an in-depth security analysis as the systems also provide functions that are required for the safe operation of the aircraft. This network design evolution, however, resulted in an iterative and continuous adaption of existing network solutions as these have not been developed from scratch.