Blockchain based secure message dissemination in vehicular networks

Vehicular ad-hoc networks (VANETs) are one of the key elements in Intelligent Transportation System (ITS) to enable information exchange among vehicles and Roadside Units (RSUs) via vehicle-to-vehicle (V2V) and vehicle-to- nfrastructure (V2I) communications. With continuously increasing number of vehicles on road, there are numerous security and privacy challenges associated with VANETs. Communication among vehicles is needed to be secure and bandwidth efficient. Also, the messages exchanged between vehicles must be authentic so as to maintain a trusted network in a privacy-preserving manner. Furthermore, a sustainable economic model is required to incentivise honest and cooperative vehicles. Traditional security and privacy solutions in centralised networks are not applicable to VANETs due to its distributed nature, heterogeneity, high mobility and low latency requirements. Meanwhile, the new development of blockchain has been attracting significant interests due to its key features including consensus to evaluate message credibility and immutable storage in distributed ledger, which provides an alternative solution to the security and privacy challenges in VANETs. This thesis aims to present blockchain solutions for the security and privacy of VANETs meeting the stringent requirements of low latency and bandwidth-efficient message dissemination. VANETs are simulated in OMNeT++ to validate the proposed solutions. Specifically, two novel blockchain consensus algorithms have been developed for message authentication and relay selection in presence of malicious vehicles. The first employs a voting based message validation and relay selection, which reduces the failure rate in message validation by 11% as compared to reputation based consensus. The second utilises federated learning supported by blockchain as a better privacy-preserving solution, which is 65.2% faster than the first voting based solution. Both approaches include blockchain-based incentive mechanisms and game theory analysis to observe strategic behaviour of honest and malicious vehicles. To further study the privacy aspect of vehicular networks, the integration of blockchain with physical layer security is also theoretically analysed in Vehicle-to-Everything (V2X) communications scenarios. The integration results in 8.2 Mbps increased goodput as compared to the blockchain solution alone. In essence, our research work shows that blockchain can offer better control and security, as compared to centralised solutions, if properly adjusted according to the application and network requirements. Thus, the proposed solutions can provide guidelines for practically feasible application of blockchain in vehicular networks

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Building Security Protocols Against Powerful Adversaries

As our sensitive data is increasingly carried over the Internet and stored remotely, security in communications becomes a fundamental requirement. Yet, today's security practices are designed around assumptions the validity of which is being challenged. In this thesis we design new security mechanisms for certain scenarios where traditional security assumptions do not hold. First, we design secret-agreement protocols for wireless networks, where the security of the secrets does not depend on assumptions about the computational limitations of adversaries. Our protocols leverage intrinsic characteristics of the wireless to enable nodes to agree on common pairwise secrets that are secure against computationally unconstrained adversaries. Through testbed and simulation experimentation, we show that it is feasible in practice to create thousands of secret bits per second. Second, we propose a traffic anonymization scheme for wireless networks. Our protocol aims in providing anonymity in a fashion similar to Tor - yet being resilient to computationally unbounded adversaries - by exploiting the security properties of our secret-agreement. Our analysis and simulation results indicate that our scheme can offer a level of anonymity comparable to the level of anonymity that Tor does. Third, we design a lightweight data encryption protocol for protecting against computationally powerful adversaries in wireless sensor networks. Our protocol aims in increasing the inherent weak security that network coding naturally offers, at a low extra overhead. Our extensive simulation results demonstrate the additional security benefits of our approach. Finally, we present a steganographic mechanism for secret message exchange over untrustworthy messaging service providers. Our scheme masks secret messages into innocuous texts, aiming in hiding the fact that secret message exchange is taking place. Our results indicate that our schemes succeeds in communicating hidden information at non-negligible rates

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

On Privacy-Enhanced Distributed Analytics in Online Social Networks

More than half of the world's population benefits from online social network (OSN) services. A considerable part of these services is mainly based on applying analytics on user data to infer their preferences and enrich their experience accordingly. At the same time, user data is monetized by service providers to run their business models. Therefore, providers tend to extensively collect (personal) data about users. However, this data is oftentimes used for various purposes without informed consent of the users. Providers share this data in different forms with third parties (e.g., data brokers). Moreover, user sensitive data was repeatedly a subject of unauthorized access by malicious parties. These issues have demonstrated the insufficient commitment of providers to user privacy, and consequently, raised users' concerns. Despite the emergence of privacy regulations (e.g., GDPR and CCPA), recent studies showed that user personal data collection and sharing sensitive data are still continuously increasing. A number of privacy-friendly OSNs have been proposed to enhance user privacy by reducing the need for central service providers. However, this improvement in privacy protection usually comes at the cost of losing social connectivity and many analytics-based services of the wide-spread OSNs. This dissertation addresses this issue by first proposing an approach to privacy-friendly OSNs that maintains established social connections. Second, approaches that allow users to collaboratively apply distributed analytics while preserving their privacy are presented. Finally, the dissertation contributes to better assessment and mitigation of the risks associated with distributed analytics. These three research directions are treated through the following six contributions. Conceptualizing Hybrid Online Social Networks: We conceptualize a hybrid approach to privacy-friendly OSNs, HOSN. This approach combines the benefits of using COSNs and DOSN. Users can maintain their social experience in their preferred COSN while being provided with additional means to enhance their privacy. Users can seamlessly post public content or private content that is accessible only by authorized users (friends) beyond the reach of the service providers. Improving the Trustworthiness of HOSNs: We conceptualize software features to address users' privacy concerns in OSNs. We prototype these features in our HOSN}approach and evaluate their impact on the privacy concerns and the trustworthiness of the approach. Also, we analyze the relationships between four important aspects that influence users' behavior in OSNs: privacy concerns, trust beliefs, risk beliefs, and the willingness to use. Privacy-Enhanced Association Rule Mining: We present an approach to enable users to apply efficiently privacy-enhanced association rule mining on distributed data. This approach can be employed in DOSN and HOSN to generate recommendations. We leverage a privacy-enhanced distributed graph sampling method to reduce the data required for the mining and lower the communication and computational overhead. Then, we apply a distributed frequent itemset mining algorithm in a privacy-friendly manner. Privacy Enhancements on Federated Learning (FL): We identify several privacy-related issues in the emerging distributed machine learning technique, FL. These issues are mainly due to the centralized nature of this technique. We discuss tackling these issues by applying FL in a hierarchical architecture. The benefits of this approach include a reduction in the centralization of control and the ability to place defense and verification methods more flexibly and efficiently within the hierarchy. Systematic Analysis of Threats in Federated Learning: We conduct a critical study of the existing attacks in FL to better understand the actual risk of these attacks under real-world scenarios. First, we structure the literature in this field and show the research foci and gaps. Then, we highlight a number of issues in (1) the assumptions commonly made by researchers and (2) the evaluation practices. Finally, we discuss the implications of these issues on the applicability of the proposed attacks and recommend several remedies. Label Leakage from Gradients: We identify a risk of information leakage when sharing gradients in FL. We demonstrate the severity of this risk by proposing a novel attack that extracts the user annotations that describe the data (i.e., ground-truth labels) from gradients. We show the high effectiveness of the attack under different settings such as different datasets and model architectures. We also test several defense mechanisms to mitigate this attack and conclude the effective ones

Continuous trust management frameworks : concept, design and characteristics

PhD ThesisA Trust Management Framework is a collection of technical components and governing rules and contracts to establish secure, confidential, and Trustworthy transactions among the Trust Stakeholders whether they are Users, Service Providers, or Legal Authorities. Despite the presence of many Trust Frameworks projects, they still fail at presenting a mature Framework that can be Trusted by all its Stakeholders. Particularly speaking, most of the current research focus on the Security aspects that may satisfy some Stakeholders but ignore other vital Trust Properties like Privacy, Legal Authority Enforcement, Practicality, and Customizability. This thesis is all about understanding and utilising the state of the art technologies of Trust Management to come up with a Trust Management Framework that could be Trusted by all its Stakeholders by providing a Continuous Data Control where the exchanged data would be handled in a Trustworthy manner before and after the data release from one party to another. For that we call it: Continuous Trust Management Framework. In this thesis, we present a literature survey where we illustrate the general picture of the current research main categorise as well as the main Trust Stakeholders, Trust Challenges, and Trust Requirements. We picked few samples representing each of the main categorise in the literature of Trust Management Frameworks for detailed comparison to understand the strengths and weaknesses of those categorise. Showing that the current Trust Management Frameworks are focusing on fulfilling most of the Trust Attributes needed by the Trust Stakeholders except for the Continuous Data Control Attribute, we argued for the vitality of our proposed generic design of the Continuous Trust Management Framework. To demonstrate our Design practicality, we present a prototype implementing its basic Stakeholders like the Users, Service Providers, Identity Provider, and Auditor on top of the OpenID Connect protocol. The sample use-case of our prototype is to protect the Users’ email addresses. That is, Users would ask for their emails not to be iii shared with third parties but some Providers would act maliciously and share these emails with third parties who would, in turn, send spam emails to the victim Users. While the prototype Auditor would be able to protect and track data before their release to the Service Providers, it would not be able to enforce the data access policy after release. We later generalise our sample use-case to cover various Mass Active Attacks on Users’ Credentials like, for example, using stolen credit cards or illegally impersonating third-party identity. To protect the Users’ Credentials after release, we introduce a set of theories and building blocks to aid our Continuous Trust Framework’s Auditor that would act as the Trust Enforcement point. These theories rely primarily on analysing the data logs recorded by our prototype prior to releasing the data. To test our theories, we present a Simulation Model of the Auditor to optimise its parameters. During some of our Simulation Stages, we assumed the availability of a Data Governance Unit, DGU, that would provide hardware roots of Trust. This DGU is to be installed in the Service Providers’ server-side to govern how they handle the Users’ data. The final simulation results include a set of different Defensive Strategies’ Flavours that could be utilized by the Auditor depending on the environment where it operates. This thesis concludes with the fact that utilising Hard Trust Measures such as DGU without effective Defensive Strategies may not provide the ultimate Trust solution. That is especially true at the bootstrapping phase where Service Providers would be reluctant to adopt a restrictive technology like our proposed DGU. Nevertheless, even in the absence of the DGU technology now, deploying the developed Defensive Strategies’ Flavours that do not rely on DGU would still provide significant improvements in terms of enforcing Trust even after data release compared to the currently widely deployed Strategy: doing nothing!Public Authority for Applied Education and Training in Kuwait, PAAET

A Location Prediction-Based Helper Selection Scheme for Suspicious Eavesdroppers

This paper aims to improve security performance of data transmission with a mobile eavesdropper in a wireless network. The instantaneous channel state information (CSI) of the mobile eavesdropper is unknown to legitimate users during the communication process. Different from existing work, we intend to reduce power consumption of friendly jamming signals. Motivated by the goal, this work presents a location-based prediction scheme to predict where the eavesdropper will be later and to decide whether a friendly jamming measure should be selected against the eavesdropper. The legitimate users only take the measure when the prediction result shows that there will be a risk during data transmission. According to the proposed method, system power can be saved to a large degree. Particularly, we first derive the expression of the secrecy outage probability and set a secrecy performance target. After providing a Markov mobile model of an eavesdropper, we design a prediction scheme to predict its location, so as to decide whether to employ cooperative jamming or not, and then design a power allocation scheme and a fast suboptimal helper selection method to achieve targeted and efficient cooperative jamming. Finally, numerical simulation results demonstrate the effectiveness of the proposed schemes