Modern vehicles are no longer simply mechanical devices. Connectivity between the vehicular network and the outside world has widened the security holes that hackers can use to exploit a vehicular network. Controller Area Network (CAN), FlexRay, and automotive Ethernet are popular protocols for in-vehicle networks (IVNs) and will stay in the industry for many more years. However, these protocols were not designed with security in mind. They have several vulnerabilities, such as lack of message authentication, lack of message encryption, and an ID-based arbitration mechanism for contention resolution. Adversaries can use these vulnerabilities to launch sophisticated attacks that may lead to loss of life and damage to property. Thus, the security of the vehicles should be handled carefully. In this paper, we investigate the security vulnerabilities with in-vehicle network protocols such as CAN, automotive Ethernet, and FlexRay. A comprehensive survey on security attacks launched against in-vehicle networks is presented along with countermeasures adopted by various researchers. Various algorithms have been proposed in the past for intrusion detection in IVNs. However, those approaches have several limitations that need special attention from the research community. Blockchain is a good approach to solving the existing security issues in IVNs, and we suggest a way to improve IVN security based on a hybrid blockchain.

An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

The trustworthiness of nodes in Vehicular Ad-Hoc Networks (VANETs) is essential for disseminating truthful event messages. False messages may cause vehicles to behave in unintended ways, creating an unreliable transportation system. The efficiency and reliability of the transportation system can be obtained through trustworthy vehicular nodes providing correct event messages. In a VANET, the consensus issue can be resolved by employing blockchain. Even if we employ blockchain in a VANET, the trustworthiness of each message recorded needs to be verified separately since the blockchain itself does not guarantee the trust level of each event message. For instance, when there are multiple conflicting messages associated with a single accident on the road, a vote based on majority opinion can be considered one option for making a decision regarding the accident. In this work, we design the VANET event message clustering algorithm (VEMCA) to resolve the conflicting message problem. Furthermore, we develop a simulator for the VANET environment that demonstrates how the clustering algorithm can be used for event message validation. Experimental results show that our algorithm outperforms state-of-the-art clustering algorithms in terms of accuracy, precision, recall, f1-score, and computational time. INDEX TERMS VANET, clustering algorithm, trustworthiness, blockchain, simulator I. INTRODUCTION A. BACKGROUND VOLUME 4, 2016