Secure Authentication and Privacy-Preserving Techniques in Vehicular Ad-hoc NETworks (VANETs)

In the last decade, there has been growing interest in Vehicular Ad Hoc NETworks (VANETs). Today car manufacturers have already started to equip vehicles with sophisticated sensors that can provide many assistive features such as front collision avoidance, automatic lane tracking, partial autonomous driving, suggestive lane changing, and so on. Such technological advancements are enabling the adoption of VANETs not only to provide safer and more comfortable driving experience but also provide many other useful services to the driver as well as passengers of a vehicle. However, privacy, authentication and secure message dissemination are some of the main issues that need to be thoroughly addressed and solved for the widespread adoption/deployment of VANETs. Given the importance of these issues, researchers have spent a lot of effort in these areas over the last decade. We present an overview of the following issues that arise in VANETs: privacy, authentication, and secure message dissemination. Then we present a comprehensive review of various solutions proposed in the last 10 years which address these issues. Our survey sheds light on some open issues that need to be addressed in the future

Towards a Framework for Preserving Privacy in VANET

Vehicular Ad-hoc Network (VANET) is envisioned as an integral part of the Intelligent Transportation Systems as it promises various services and benefits such as road safety, traffic efficiency, navigation and infotainment services. However, the security and privacy risks associated with the wireless communication are often overlooked. Messages exchanged in VANET wireless communication carry inferable Personally Identifiable Information(PII). This introduces several privacy threats that could limit the adoption of VANET. The quantification of these privacy threats is an active research area in VANET security and privacy domains. The Pseudonymisation technique is currently the most preferred solution for critical privacy threats in VANET to provide conditional anonymous authentication. In the existing literature, several Pseudonym Changing Schemes(PCS) have been proposed as effective de-identification approaches to prevent the inference of PII. However, for various reasons, none of the proposed schemes received public acceptance. Moreover, one of the open research challenges is to compare different PCSs under varying circumstances with a set of standardized experimenting parameters and consistent metrics. In this research, we propose a framework to assess the effectiveness of PCSs in VANET with a systematic approach. This comprehensive equitable framework consists of a variety of building blocks which are segmented into correlated sub-domains named Mobility Models, Adversary Models, and Privacy Metrics. Our research introduces a standard methodology to evaluate and compare VANET PCSs using a generic simulation setup to obtain optimal, realistic and most importantly, consistent results. This road map for the simulation setup aims to help the research \& development community to develop, assess and compare the PCS with standard set of parameters for proper analysis and reporting of new PCSs. The assessment of PCS should not only be equitable but also realistic and feasible. Therefore, the sub-domains of the framework need coherent as well as practically applicable characteristics. The Mobility Model is the layout of the traffic on the road which has varying features such as traffic density and traffic scenarios based on the geographical maps. A diverse range of Adversary Models is important for pragmatic evaluation of the PCSs which not only considers the presence of global passive adversary but also observes the effect of intelligent and strategic \u27local attacker\u27 placements. The biggest challenge in privacy measurement is the fact that it is a context-based evaluation. In the literature, the PCSs are evaluated using either user-oriented or adversary-oriented metrics. Under all circumstances, the PCSs should be assessed from both user and adversary perspectives. Using this framework, we determined that a local passive adversary can be strong based on the attacking capabilities. Therefore, we propose two intelligent adversary placements which help in privacy assessment with realistic adversary modelling. When the existing PCSs are assessed with our systematic approach, consistent models and metrics, we identified the privacy vulnerabilities and the limitations of existing PCSs. There was a need for comprehensive PCS which consider the context of the vehicles and the changing traffic patterns in the neighbourhood. Consequently, we developed a Context-Aware \& Traffic Based PCS that focuses on increasing the overall rate of confusion for the adversary and to reduce deterministic information regarding the pseudonym change. It is achieved by increasing the number of dynamic attributes in the proposed PCS for inference of the changing pattern of the pseudonyms. The PCS increases the anonymity of the vehicle by having the synchronized pseudonym changes. The details given under the sub-domains of the framework solidifies our findings to strengthen the privacy assessment of our proposed PCS

Software Protection and Secure Authentication for Autonomous Vehicular Cloud Computing

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC. In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our vision of a layer-based approach to thoroughly study state-of-the-art literature in the realm of AVs. Particularly, we examined some cyber-attacks and compared their promising mitigation strategies from our perspective. Then, we focused on two security issues involving AVCC: software protection and authentication. For the first problem, our concern is protecting client’s programs executed on remote AVCC resources. Such a usage scenario is susceptible to information leakage and reverse-engineering. Hence, we proposed compiler-based obfuscation techniques. What distinguishes our techniques, is that they are generic and software-based and utilize the intermediate representation, hence, they are platform agnostic, hardware independent and support different high level programming languages. Our results demonstrate that the control-flow of obfuscated code versions are more complicated making it unintelligible for timing side-channels. For the second problem, we focus on protecting AVCC from unauthorized access or intrusions, which may cause misuse or service disruptions. Therefore, we propose a strong privacy-aware authentication technique for users accessing AVCC services or vehicle sharing their resources with the AVCC. Our technique modifies robust function encryption, which protects stakeholder’s confidentiality and withstands linkability and “known-ciphertexts” attacks. Thus, we utilize an authentication server to search and match encrypted data by performing dot product operations. Additionally, we developed another lightweight technique, based on KNN algorithm, to authenticate vehicles at computationally limited charging stations using its owner’s encrypted iris data. Our security and privacy analysis proved that our schemes achieved privacy-preservation goals. Our experimental results showed that our schemes have reasonable computation and communications overheads and efficiently scalable

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

Project BeARCAT : Baselining, Automation and Response for CAV Testbed Cyber Security : Connected Vehicle & Infrastructure Security Assessment

Connected, software-based systems are a driver in advancing the technology of transportation systems. Advanced automated and autonomous vehicles, together with electrification, will help reduce congestion, accidents and emissions. Meanwhile, vehicle manufacturers see advanced technology as enhancing their products in a competitive market. However, as many decades of using home and enterprise computer systems have shown, connectivity allows a system to become a target for criminal intentions. Cyber-based threats to any system are a problem; in transportation, there is the added safety implication of dealing with moving vehicles and the passengers within

Vehicular Networks and Outdoor Pedestrian Localization

This thesis focuses on vehicular networks and outdoor pedestrian localization. In particular, it targets secure positioning in vehicular networks and pedestrian localization for safety services in outdoor environments. The former research topic must cope with three major challenges, concerning users’ privacy, computational costs of security and the system trust on user correctness. This thesis addresses those issues by proposing a new lightweight privacy-preserving framework for continuous tracking of vehicles. The proposed solution is evaluated in both dense and sparse vehicular settings through simulation and experiments in real-world testbeds. In addition, this thesis explores the benefit given by the use of low frequency bands for the transmission of control messages in vehicular networks. The latter topic is motivated by a significant number of traffic accidents with pedestrians distracted by their smartphones. This thesis proposes two different localization solutions specifically for pedestrian safety: a GPS-based approach and a shoe-mounted inertial sensor method. The GPS-based solution is more suitable for rural and suburban areas while it is not applicable in dense urban environments, due to large positioning errors. Instead the inertial sensor approach overcomes the limitations of previous technique in urban environments. Indeed, by exploiting accelerometer data, this architecture is able to precisely detect the transitions from safe to potentially unsafe walking locations without the need of any absolute positioning systems

Efficient and Secure ECDSA Algorithm and its Applications: A Survey

Public-key cryptography algorithms, especially elliptic curve cryptography (ECC)and elliptic curve digital signature algorithm (ECDSA) have been attracting attention frommany researchers in different institutions because these algorithms provide security andhigh performance when being used in many areas such as electronic-healthcare, electronicbanking,electronic-commerce, electronic-vehicular, and electronic-governance. These algorithmsheighten security against various attacks and the same time improve performanceto obtain efficiencies (time, memory, reduced computation complexity, and energy saving)in an environment of constrained source and large systems. This paper presents detailedand a comprehensive survey of an update of the ECDSA algorithm in terms of performance,security, and applications