Connected and automated vehicles (CAVs) as a part of Intelligent Transportation Systems (ITS) are projected to revolutionise the transportation industry, primarily by allowing real-time and seamless information exchange of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). However, these connectivity and automation are expected to offer vast numbers of benefits, new challenges in terms of safety, security and privacy also emerge. CAVs continue to rely heavily on their sensor readings, the input obtained from other vehicles and the road side units to inspect roadways. Consequently, anomalous reading of sensors triggered by malicious cyber attacks may lead to fatal consequences. Hence, like all other safety-critical applications, in CAVs also, reliable and secure information dissemination is of utmost importance. As a result, real time detection of anomaly along with identifying the source is a pre-requisite for mass deployment of CAVs. Motivated by this safety concerns in CAVs, we develop an efficient anomaly detection method through the combination of Bayesian deep learning (BDL) with discrete wavelet transform (DWT) to improve the safety and security in CAVs. In particular, DWT is used to smooth sensor reading of a CAV and then feed the data to a BDL module for analysis of the detection and identification of anomalous sensor behavior/data points caused by either malicious cyber attacks or faulty vehicle sensors. Our numerical experiments show that the proposed method demonstrates significant improvement in detection anomalies in terms of accuracy, sensitivity, precision, and F1-score evaluation metrics. For these metrics, the proposed method shows an average performance gain of $7.95\%$, $9\%$, $8.77\%$ and $7.33\%$, respectively when compared with Convolutional Neural Network (CNN-1D), and when compared with BDL, the corresponding numbers are $5\%$, $7.9\%$, $7.54\%$ and $4.1\%$ respectively.

En la última década con el surgimiento del paradigma de Internet de las Cosas y su gran acogida y expansión en diferentes dominios de aplicación, han surgido nuevos retos que dejan ver una gran problemática en lo referente a la gestión de la seguridad de la información, los cuales representan un riesgo importante para las organizaciones y los usuarios finales que ya están implementando este paradigma en sus actividades y procesos. Frente a esta problemática se han venido desarrollando algunos estudios desde diferentes puntos de vista, abarcando varios dominios de aplicación, pero sin presentar aún una visión unificada de cómo afrontar los riesgos asociados a la implementación de tecnologías IoT en las organizaciones. En el presente estudio se ha recopilado diversos trabajos de la última década donde se expone diferentes vulnerabilidades y amenazas que suelen presentarse de forma específica en entornos que implementan Internet de las Cosas, así como diferentes propuestas para gestionar la seguridad de información frente a los retos emergentes.

A mechanism for trust management is required in vehicular ad hoc networks (VANETs) to record the behavior of the vehicles. However, it is a complex matter to follow the historical behavior of the vehicles, since their identities are protected by pseudonyms, which are constantly changing to prevent tracking. In our proposal based on a centralized reputation system, being a vehicle well behaved in the opportunistic forwarding of messages comprises two components: reputation for messages creation and for messages forwarding. The vehicles can forward messages at a given time, and the types of messages that are sent in each transaction may require a vehicle reputation score. In this case, the decision of a vehicle to accept a message is based on the reputation of the sender vehicle that created or forwarded the message. We describe the reputation system through the first three levels of a functional ontology and finite state machines. We evaluate the system by means of simulations in an urban scenario to obtain the reputations of forwarding and generation of messages, varying the number of misbehaving vehicles. It was found that a centralized reputation scheme reflects the behavior of the vehicles insofar as feedback is delivered to the reputation server.

Security mechanisms recommended by VANET standardization bodies are not sufficient to stop authorized vehicles from sending bogus or fake messages that in the worst cases could put people's life at risk. There are efforts in evaluating the behaviour of the vehicles in VANETs, based on reputation mechanisms. However, the most of them compromise the privacy, the messages are linkable and no anonymous. Our proposal ARS: Anonymous Reputation System for VANET focuses on a centralized reputation system privacy-preserving using pseudonyms and giving reputation level to different behavioral aspects of the vehicle. A reputation server collects feedback from the vehicles through a Road-Side Unit or vehicles with LTE/4G/5G connection and updates the vehicles reputation level by matching the pseudoidentities with its real identity. Considering the amount of negative feedback, ARS defines two security states to adapt the applications to the real security risk. We implement and evaluate the performance of ARS through a set of simulation experiments, based on an opportunistic message forwarding application. Results showed that the calculating reputation level of vehicles could help reduce the dissemination of fake messages.

Pseudonym change schemes have been studied in VANETs to guarantee the property of untraceability of the vehicles. This paper proposes a new condition based on the assumption that if there are no malicious vehicles around when a synchronous change triggers, there is no risk of tracking, and the pseudonym change defers until a time threshold. Thus, our proposal based on a synchronous strategy to increase the anonymity set, adds to the change condition, the checking of the reputation status of the neighbouring vehicles. The reputation status is the result of discretizing the reputation score of the vehicles and it is part of the periodically broadcast beacon messages. The proposal was evaluated through simulations using a real scenario. The results showed an optimization in the use of pseudonyms maintaining similar anonymity levels with other schemes.

In the future, wireless access in vehicular environments (WAVE) will provide the foundation for a broad range of intelligent applications in transport, related to safety, comfort and, traffic management, to mention a few. Opportunities to instantaneously advertise and disseminate services arise with the technological advances of the vehicular networks. Points of interest (POI) to drivers and passengers, e.g, shops and gas stations, will broadcast advertisements with local services and facilities to the nearby vehicles by exploiting the road-side unit (RSU) infrastructure and comfort applications. However, RSU access coverage limitation or packet losses can prevent users (drivers or passengers) who are genuinely interested in services from finding them, even with a short distance from the POI. To improve the service discovery, we propose OSDP (opportunistic service discovery protocol), a protocol based on opportunistic vehicle contact and a store-and-forward technique to discover and advertise services in vehicular networks. OSDP is a lightweight and alternative protocol intended for location-aware applications. It supports distributed services fully independent from Internet access. In this paper we describe the key design concepts of OSDP and demonstrate its feasibility by a concept proof test bed. We perform two experiments evaluating the success rate of: i) capturing and storing service messages and ii) searching for a service of interest and receiving reply messages containing it. OSPD feasibility was demonstrated for different vehicle densities and for very short time connections. Results indicate that a vehicle can capture 100% of messages under the coverage of seven POI sending advertisement messages every second, with a contact time of 1.2s. Moreover, a user can find a service with 95% of success rate by querying one neighbour vehicle.