A Protocol for Authentication with Multiple Levels of Anonymity (AMLA) in VANETs

The basic requirements for secure communication in a vehicular ad hoc network (VANET) are anonymous authentication with source non-repudiation and integrity. The existing security protocols in VANETs do not differentiate between the anonymity requirements of different vehicles and the level of anonymity provided by these protocols is the same for all the vehicles in a network. To provide high level of anonymity, the resource requirements of security protocol would also be high. Hence, in a resource constrained VANET, it is necessary to differentiate between the anonymity requirements of different vehicles and to provide the level of anonymity to a vehicle as per its requirement. In this paper, we have proposed a novel protocol for authentication which can provide multiple levels of anonymity in VANETs. The protocol makes use of identity based signature mechanism and pseudonyms to implement anonymous authentication with source non-repudiation and integrity. By controlling the number of pseudonyms issued to a vehicle and the lifetime of each pseudonym for a vehicle, the protocol is able to control the level of anonymity provided to a vehicle. In addition, the protocol includes a novel pseudonym issuance policy using which the protocol can ensure the uniqueness of a newly generated pseudonym by checking only a very small subset of the set of pseudonyms previously issued to all the vehicles. The protocol cryptographically binds an expiry date to each pseudonym, and in this way, enforces an implicit revocation for the pseudonyms. Analytical and simulation results confirm the effectiveness of the proposed protocol

A Trust Management Framework for Vehicular Ad Hoc Networks

The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers