MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

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

Universal Skeptic Binder-Droid - Towards Arresting Malicious Communication of Colluding Apps in Android

Since its first release, Android has been increasingly adopted by people and companies worldwide. It is currently estimated that around 1.1 billion Android devices are in use. Even though Android was built with Security principles and comes with a sound security model, it is a favorite target for malware authors. McAfee observed a 76% year on year growth in Android malware during the year 2014 alone. Thus malware is a predominant threat in Android ecosystem. A common attack vector for Android malware is the use of colluding apps. Colluding apps involve two or more applications and operate in two phases. In Phase 1, one application steals private sensitive data of the user In Phase 2, the same application sends the data to another application via covert communication channels. There are several covert channels in Android frameworks. Until now, several solutions in the literature have focused on preventing the extraction of sensitive data from the phone. We, to the best of our knowledge, are the first to stop the flow of sensitive info via the covert channels. We propose, Universal Skeptic Binder-Droid, an enhanced Binder module which enforces policies regarding the use of communication channels and prevents apps from colluding. With our proposed system, we have the added advantage of dynamically configuring policies at run time. Our initial implementation and results on our test bed reflect on the effectiveness and the ease of use of such a system

Data security in cloud storage services

Cloud Computing is considered to be the next-generation architecture for ICT where it moves the application software and databases to the centralized large data centers. It aims to offer elastic IT services where clients can benefit from significant cost savings of the pay-per-use model and can easily scale up or down, and do not have to make large investments in new hardware. However, the management of the data and services in this cloud model is under the control of the provider. Consequently, the cloud clients have less control over their outsourced data and they have to trust cloud service provider to protect their data and infrastructure from both external and internal attacks. This is especially true with cloud storage services. Nowadays, users rely on cloud storage as it offers cheap and unlimited data storage that is available for use by multiple devices (e.g. smart phones, tablets, notebooks, etc.). Besides famous cloud storage providers, such as Amazon, Google, and Microsoft, more and more third-party cloud storage service providers are emerging. These services are dedicated to offering more accessible and user friendly storage services to cloud customers. Examples of these services include Dropbox, Box.net, Sparkleshare, UbuntuOne or JungleDisk. These cloud storage services deliver a very simple interface on top of the cloud storage provided by storage service providers. File and folder synchronization between different machines, sharing files and folders with other users, file versioning as well as automated backups are the key functionalities of these emerging cloud storage services. Cloud storage services have changed the way users manage and interact with data outsourced to public providers. With these services, multiple subscribers can collaboratively work and share data without concerns about their data consistency, availability and reliability. Although these cloud storage services offer attractive features, many customers have not adopted these services. Since data stored in these services is under the control of service providers resulting in confidentiality and security concerns and risks. Therefore, using cloud storage services for storing valuable data depends mainly on whether the service provider can offer sufficient security and assurance to meet client requirements. From the way most cloud storage services are constructed, we can notice that these storage services do not provide users with sufficient levels of security leading to an inherent risk on users\u27 data from external and internal attacks. These attacks take the form of: data exposure (lack of data confidentiality); data tampering (lack of data integrity); and denial of data (lack of data availability) by third parties on the cloud or by the cloud provider himself. Therefore, the cloud storage services should ensure the data confidentiality in the following state: data in motion (while transmitting over networks), data at rest (when stored at provider\u27s disks). To address the above concerns, confidentiality and access controllability of outsourced data with strong cryptographic guarantee should be maintained. To ensure data confidentiality in public cloud storage services, data should be encrypted data before it is outsourced to these services. Although, users can rely on client side cloud storage services or software encryption tools for encrypting user\u27s data; however, many of these services fail to achieve data confidentiality. Box, for example, does not encrypt user files via SSL and within Box servers. Client side cloud storage services can intentionally/unintentionally disclose user decryption keys to its provider. In addition, some cloud storage services support convergent encryption for encrypting users\u27 data exposing it to “confirmation of a file attack. On the other hand, software encryption tools use full-disk encryption (FDE) which is not feasible for cloud-based file sharing services, because it encrypts the data as virtual hard disks. Although encryption can ensure data confidentiality; however, it fails to achieve fine-grained access control over outsourced data. Since, public cloud storage services are managed by un-trusted cloud service provider, secure and efficient fine-grained access control cannot be realized through these services as these policies are managed by storage services that have full control over the sharing process. Therefore, there is not any guarantee that they will provide good means for efficient and secure sharing and they can also deduce confidential information about the outsourced data and users\u27 personal information. In this work, we would like to improve the currently employed security measures for securing data in cloud store services. To achieve better data confidentiality for data stored in the cloud without relying on cloud service providers (CSPs) or putting any burden on users, in this thesis, we designed a secure cloud storage system framework that simultaneously achieves data confidentiality, fine-grained access control on encrypted data and scalable user revocation. This framework is built on a third part trusted (TTP) service that can be employed either locally on users\u27 machine or premises, or remotely on top of cloud storage services. This service shall encrypts users data before uploading it to the cloud and decrypts it after downloading from the cloud; therefore, it remove the burden of storing, managing and maintaining encryption/decryption keys from data owner\u27s. In addition, this service only retains user\u27s secret key(s) not data. Moreover, to ensure high security for these keys, it stores them on hardware device. Furthermore, this service combines multi-authority ciphertext policy attribute-based encryption (CP-ABE) and attribute-based Signature (ABS) for achieving many-read-many-write fine-grained data access control on storage services. Moreover, it efficiently revokes users\u27 privileges without relying on the data owner for re-encrypting massive amounts of data and re-distributing the new keys to the authorized users. It removes the heavy computation of re-encryption from users and delegates this task to the cloud service provider (CSP) proxy servers. These proxy servers achieve flexible and efficient re-encryption without revealing underlying data to the cloud. In our designed architecture, we addressed the problem of ensuring data confidentiality against cloud and against accesses beyond authorized rights. To resolve these issues, we designed a trusted third party (TTP) service that is in charge of storing data in an encrypted format in the cloud. To improve the efficiency of the designed architecture, the service allows the users to choose the level of severity of the data and according to this level different encryption algorithms are employed. To achieve many-read-many-write fine grained access control, we merge two algorithms (multi-authority ciphertext policy attribute-based encryption (MA- CP-ABE) and attribute-based Signature (ABS)). Moreover, we support two levels of revocation: user and attribute revocation so that we can comply with the collaborative environment. Last but not least, we validate the effectiveness of our design by carrying out a detailed security analysis. This analysis shall prove the correctness of our design in terms of data confidentiality each stage of user interaction with the cloud

Préserver la vie privée des individus grâce aux Systèmes Personnels de Gestion des Données

Riding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. Our work addresses these questions on three levels. First, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Second, we focus on personal computations for a specific hardware PDMS instance (i.e., secure token with mass storage of NAND Flash). In this context, we propose a scalable embedded full-text search engine to index large document collections and manage tag-based access control policies. Third, we address the problem of collective computations in a fully-distributed architecture of PDMSs. We discuss the system and security requirements and propose protocols to enable distributed query processing with strong security guarantees against an attacker mastering many colluding corrupted nodes.Surfant sur la vague des initiatives de divulgation restreinte de données et des nouvelles réglementations en matière de protection de la vie privée, le paradigme du Cloud Personnel émerge à travers une myriade de solutions proposées aux utilisateurs leur permettant de rassembler et de gérer l'ensemble de leur vie numérique. Du côté positif, cela ouvre la voie à de nouveaux services à valeur ajoutée lors du croisement de plusieurs sources de données d'un individu ou du croisement des données de plusieurs personnes. Cependant, ce changement de paradigme vers la responsabilisation de l'utilisateur soulève des questions fondamentales quant à l'adéquation des fonctionnalités et des techniques de gestion et de protection des données proposées par les solutions existantes aux utilisateurs lambda. Notre travail aborde ces questions à trois niveaux. Tout d'abord, nous passons en revue, comparons et analysons les alternatives de cloud personnel au niveau des fonctionnalités fournies et des modèles de menaces ciblés. De cette analyse, nous déduisons un ensemble général d'exigences en matière de fonctionnalité et de sécurité que tout système personnel de gestion des données (PDMS) devrait prendre en compte. Nous identifions ensuite les défis liés à la mise en œuvre d'un tel PDMS et proposons une conception préliminaire pour une architecture PDMS étendue et sécurisée de référence répondant aux exigences considérées. Ensuite, nous nous concentrons sur les calculs personnels pour une instance matérielle spécifique du PDMS (à savoir, un dispositif personnel sécurisé avec un stockage de masse de type NAND Flash). Dans ce contexte, nous proposons un moteur de recherche plein texte embarqué et évolutif pour indexer de grandes collections de documents et gérer des politiques de contrôle d'accès basées sur des étiquettes. Troisièmement, nous abordons le problème des calculs collectifs dans une architecture entièrement distribuée de PDMS. Nous discutons des exigences d'architectures système et de sécurité et proposons des protocoles pour permettre le traitement distribué des requêtes avec de fortes garanties de sécurité contre un attaquant maîtrisant de nombreux nœuds corrompus

MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust – a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies in order to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform in order to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

A multi-dimensional trust-model for dynamic, scalable and resources-efficient trust-management in social internet of things

L'internet des Objets (IoT) est un paradigme qui a rendu les objets du quotidien, intelligents en leur offrant la possibilité de se connecter à Internet, de communiquer et d'interagir. L'intégration de la composante sociale dans l'IoT a donné naissance à l'Internet des Objets Social (SIoT), qui a permis de surmonter diverse problématiques telles que l'interopérabilité et la découverte de ressources. Dans ce type d'environnement, les participants rivalisent afin d'offrir une variété de services attrayants. Certains d'entre eux ont recours à des comportements malveillants afin de propager des services de mauvaise qualité. Ils lancent des attaques, dites de confiance, et brisent les fonctionnalités de base du système. Plusieurs travaux de la littérature ont abordé ce problème et ont proposé différents modèles de confiance. La majorité d'entre eux ont tenté de réappliquer des modèles de confiance conçus pour les réseaux sociaux ou les réseaux pair-à-pair. Malgré les similitudes entre ces types de réseaux, les réseaux SIoT présentent des particularités spécifiques. Dans les SIoT, nous avons différents types d'entités qui collaborent, à savoir des humains, des dispositifs et des services. Les dispositifs peuvent présenter des capacités de calcul et de stockage très limitées et leur nombre peut atteindre des millions. Le réseau qui en résulte est complexe et très dynamique et les répercussions des attaques de confiance peuvent être plus importantes. Nous proposons un nouveau modèle de confiance, multidimensionnel, dynamique et scalable, spécifiquement conçu pour les environnements SIoT. Nous proposons, en premier lieu, des facteurs permettant de décrire le comportement des trois types de nœuds impliqués dans les réseaux SIoT et de quantifier le degré de confiance selon les trois dimensions de confiance résultantes. Nous proposons, ensuite, une méthode d'agrégation basée sur l'apprentissage automatique et l'apprentissage profond qui permet d'une part d'agréger les facteurs proposés pour obtenir un score de confiance permettant de classer les nœuds, mais aussi de détecter les types d'attaques de confiance et de les contrer. Nous proposons, ensuite, une méthode de propagation hybride qui permet de diffuser les valeurs de confiance dans le réseau, tout en remédiant aux inconvénients des méthodes centralisée et distribuée. Cette méthode permet d'une part d'assurer la scalabilité et le dynamisme et d'autre part, de minimiser la consommation des ressources. Les expérimentations appliquées sur des de données synthétiques nous ont permis de valider le modèle proposé.The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by giving them the ability to connect to the Internet, communicate and interact. The integration of the social component in the IoT has given rise to the Social Internet of Things (SIoT), which has overcome various issues such as interoperability, navigability and resource/service discovery. In this type of environment, participants compete to offer a variety of attractive services. Some of them resort to malicious behavior to propagate poor quality services. They launch so-called Trust-Attacks (TA) and break the basic functionality of the system. Several works in the literature have addressed this problem and have proposed different trust-models. Most of them have attempted to adapt and reapply trust models designed for traditional social networks or peer-to-peer networks. Despite the similarities between these types of networks, SIoT ones have specific particularities. In SIoT, there are different types of entities that collaborate: humans, devices, and services. Devices can have very limited computing and storage capacities, and their number can be as high as a few million. The resulting network is complex and highly dynamic, and the impact of Trust-Attacks can be more compromising. In this work, we propose a Multidimensional, Dynamic, Resources-efficient and Scalable trust-model that is specifically designed for SIoT environments. We, first, propose features to describe the behavior of the three types of nodes involved in SIoT networks and to quantify the degree of trust according to the three resulting Trust-Dimensions. We propose, secondly, an aggregation method based on Supervised Machine-Learning and Deep Learning that allows, on the one hand, to aggregate the proposed features to obtain a trust score allowing to rank the nodes, but also to detect the different types of Trust-Attacks and to counter them. We then propose a hybrid propagation method that allows spreading trust values in the network, while overcoming the drawbacks of centralized and distributed methods. The proposed method ensures scalability and dynamism on the one hand, and minimizes resource consumption (computing and storage), on the other. Experiments applied to synthetic data have enabled us to validate the resilience and performance of the proposed model

Applications of Context-Aware Systems in Enterprise Environments

In bring-your-own-device (BYOD) and corporate-owned, personally enabled (COPE) scenarios, employees’ devices store both enterprise and personal data, and have the ability to remotely access a secure enterprise network. While mobile devices enable users to access such resources in a pervasive manner, it also increases the risk of breaches for sensitive enterprise data as users may access the resources under insecure circumstances. That is, access authorizations may depend on the context in which the resources are accessed. In both scenarios, it is vital that the security of accessible enterprise content is preserved. In this work, we explore the use of contextual information to influence access control decisions within context-aware systems to ensure the security of sensitive enterprise data. We propose several context-aware systems that rely on a system of sensors in order to automatically adapt access to resources based on the security of users’ contexts. We investigate various types of mobile devices with varying embedded sensors, and leverage these technologies to extract contextual information from the environment. As a direct consequence, the technologies utilized determine the types of contextual access control policies that the context-aware systems are able to support and enforce. Specifically, the work proposes the use of devices pervaded in enterprise environments such as smartphones or WiFi access points to authenticate user positional information within indoor environments as well as user identities

Personal Data Management Systems: The security and functionality standpoint

International audienceRiding the wave of smart disclosure initiatives and new privacy-protection regulations, the Personal Cloud paradigm is emerging through a myriad of solutions offered to users to let them gather and manage their whole digital life. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. These questions must be answered in order to limit the risk of seeing such solutions adopted only by a handful of users and thus leaving the Personal Cloud paradigm to become no more than one of the latest missed attempts to achieve a better regulation of the management of personal data. To this end, we review, compare and analyze personal cloud alternatives in terms of the functionalities they provide and the threat models they target. From this analysis, we derive a general set of functionality and security requirements that any Personal Data Management System (PDMS) should consider. We then identify the challenges of implementing such a PDMS and propose a preliminary design for an extensive and secure PDMS reference architecture satisfying the considered requirements. Finally, we discuss several important research challenges remaining to be addressed to achieve a mature PDMS ecosystem