Sybil tolerance and probabilistic databases to compute web services trust

© Springer International Publishing Switzerland 2015. This paper discusses how Sybil attacks can undermine trust management systems and how to respond to these attacks using advanced techniques such as credibility and probabilistic databases. In such attacks end-users have purposely different identities and hence, can provide inconsistent ratings over the same Web Services. Many existing approaches rely on arbitrary choices to filter out Sybil users and reduce their attack capabilities. However this turns out inefficient. Our approach relies on non-Sybil credible users who provide consistent ratings over Web services and hence, can be trusted. To establish these ratings and debunk Sybil users techniques such as fuzzy-clustering, graph search, and probabilistic databases are adopted. A series of experiments are carried out to demonstrate robustness of our trust approach in presence of Sybil attacks

Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv

Risk assessment in centralized and decentralized online social network.

One of the main concerns in centralized and decentralized OSNs is related to the fact that OSNs users establish new relationships with unknown people with the result of exposing a huge amount of personal data. This can attract the variety of attackers that try to propagate malwares and malicious items in the network to misuse the personal information of users. Therefore, there have been several research studies to detect specific kinds of attacks by focusing on the topology of the graph [159, 158, 32, 148, 157]. On the other hand, there are several solutions to detect specific kinds of attackers based on the behavior of users. But, most of these approaches either focus on just the topology of the graph [159, 158] or the detection of anomalous users by exploiting supervised learning techniques [157, 47, 86, 125]. However, we have to note that the main issue of supervised learning is that they are not able to detect new attacker's behaviors, since the classifier is trained based on the known behavioral patterns. Literature also offers approaches to detect anomalous users in OSNs that use unsupervised learning approaches [150, 153, 36, 146] or a combination of supervised and unsupervised techniques [153]. But, existing attack defenses are designed to cope with just one specific type of attack. Although several solutions to detect specific kinds of attacks have been recently proposed, there is no general solution to cope with the main privacy/security attacks in OSNs. In such a scenario, it would be very beneficial to have a solution that can cope with the main privacy/security attacks that can be perpetrated using the social network graph. Our main contribution is proposing a unique unsupervised approach that helps OSNs providers and users to have a global understanding of risky users and detect them. We believe that the core of such a solution is a mechanism able to assign a risk score to each OSNs account. Over the last three years, we have done significant research efforts in analyzing user's behavior to detect risky users included some kinds of well known attacks in centralized and decentralized online social networks. Our research started by proposing a risk assessment approach based on the idea that the more a user behavior diverges from normal behavior, the more it should be considered risky. In our proposed approach, we monitor and analyze the combination of interaction or activity patterns and friendship patterns of users and build the risk estimation model in order to detect and identify those risky users who follow the behavioral patterns of attackers. Since, users in OSNs follow different behavioral patterns, it is not possible to define a unique standard behavioral model that fits all OSNs users' behaviors. Towards this goal, we propose a two-phase risk assessment approach by grouping users in the first phase to find similar users that share the same behavioral patterns and, then in the second phase, for each identified group, building some normal behavior models and compute for each user the level of divergency from these normal behaviors. Then, we extend this approach for Decentralized Online Social Networks (i.e., DOSNs). In the following of this approach, we propose a solution in defining a risk measure to help users in OSNs to judge their direct contacts so as to avoid friendship with malicious users. Finally, we monitor dynamically the friendship patterns of users in a large social graph over time for any anomalous changes reflecting attacker's behaviors. In this thesis, we will describe all the solutions that we proposed

Risk assessment in centralized and decentralized online social network.

One of the main concerns in centralized and decentralized OSNs is related to the fact that OSNs users establish new relationships with unknown people with the result of exposing a huge amount of personal data. This can attract the variety of attackers that try to propagate malwares and malicious items in the network to misuse the personal information of users. Therefore, there have been several research studies to detect specific kinds of attacks by focusing on the topology of the graph [159, 158, 32, 148, 157]. On the other hand, there are several solutions to detect specific kinds of attackers based on the behavior of users. But, most of these approaches either focus on just the topology of the graph [159, 158] or the detection of anomalous users by exploiting supervised learning techniques [157, 47, 86, 125]. However, we have to note that the main issue of supervised learning is that they are not able to detect new attacker's behaviors, since the classifier is trained based on the known behavioral patterns. Literature also offers approaches to detect anomalous users in OSNs that use unsupervised learning approaches [150, 153, 36, 146] or a combination of supervised and unsupervised techniques [153]. But, existing attack defenses are designed to cope with just one specific type of attack. Although several solutions to detect specific kinds of attacks have been recently proposed, there is no general solution to cope with the main privacy/security attacks in OSNs. In such a scenario, it would be very beneficial to have a solution that can cope with the main privacy/security attacks that can be perpetrated using the social network graph. Our main contribution is proposing a unique unsupervised approach that helps OSNs providers and users to have a global understanding of risky users and detect them. We believe that the core of such a solution is a mechanism able to assign a risk score to each OSNs account. Over the last three years, we have done significant research efforts in analyzing user's behavior to detect risky users included some kinds of well known attacks in centralized and decentralized online social networks. Our research started by proposing a risk assessment approach based on the idea that the more a user behavior diverges from normal behavior, the more it should be considered risky. In our proposed approach, we monitor and analyze the combination of interaction or activity patterns and friendship patterns of users and build the risk estimation model in order to detect and identify those risky users who follow the behavioral patterns of attackers. Since, users in OSNs follow different behavioral patterns, it is not possible to define a unique standard behavioral model that fits all OSNs users' behaviors. Towards this goal, we propose a two-phase risk assessment approach by grouping users in the first phase to find similar users that share the same behavioral patterns and, then in the second phase, for each identified group, building some normal behavior models and compute for each user the level of divergency from these normal behaviors. Then, we extend this approach for Decentralized Online Social Networks (i.e., DOSNs). In the following of this approach, we propose a solution in defining a risk measure to help users in OSNs to judge their direct contacts so as to avoid friendship with malicious users. Finally, we monitor dynamically the friendship patterns of users in a large social graph over time for any anomalous changes reflecting attacker's behaviors. In this thesis, we will describe all the solutions that we proposed

LightChain: A DHT-based Blockchain for Resource Constrained Environments

As an append-only distributed database, blockchain is utilized in a vast variety of applications including the cryptocurrency and Internet-of-Things (IoT). The existing blockchain solutions have downsides in communication and storage efficiency, convergence to centralization, and consistency problems. In this paper, we propose LightChain, which is the first blockchain architecture that operates over a Distributed Hash Table (DHT) of participating peers. LightChain is a permissionless blockchain that provides addressable blocks and transactions within the network, which makes them efficiently accessible by all the peers. Each block and transaction is replicated within the DHT of peers and is retrieved in an on-demand manner. Hence, peers in LightChain are not required to retrieve or keep the entire blockchain. LightChain is fair as all of the participating peers have a uniform chance of being involved in the consensus regardless of their influence such as hashing power or stake. LightChain provides a deterministic fork-resolving strategy as well as a blacklisting mechanism, and it is secure against colluding adversarial peers attacking the availability and integrity of the system. We provide mathematical analysis and experimental results on scenarios involving 10K nodes to demonstrate the security and fairness of LightChain. As we experimentally show in this paper, compared to the mainstream blockchains like Bitcoin and Ethereum, LightChain requires around 66 times less per node storage, and is around 380 times faster on bootstrapping a new node to the system, while each LightChain node is rewarded equally likely for participating in the protocol

Enhancing trustability in MMOGs environments

Massively Multiplayer Online Games (MMOGs; e.g., World of Warcraft), virtual worlds (VW; e.g., Second Life), social networks (e.g., Facebook) strongly demand for more autonomic, security, and trust mechanisms in a way similar to humans do in the real life world. As known, this is a difficult matter because trusting in humans and organizations depends on the perception and experience of each individual, which is difficult to quantify or measure. In fact, these societal environments lack trust mechanisms similar to those involved in humans-to-human interactions. Besides, interactions mediated by compute devices are constantly evolving, requiring trust mechanisms that keep the pace with the developments and assess risk situations. In VW/MMOGs, it is widely recognized that users develop trust relationships from their in-world interactions with others. However, these trust relationships end up not being represented in the data structures (or databases) of such virtual worlds, though they sometimes appear associated to reputation and recommendation systems. In addition, as far as we know, the user is not provided with a personal trust tool to sustain his/her decision making while he/she interacts with other users in the virtual or game world. In order to solve this problem, as well as those mentioned above, we propose herein a formal representation of these personal trust relationships, which are based on avataravatar interactions. The leading idea is to provide each avatar-impersonated player with a personal trust tool that follows a distributed trust model, i.e., the trust data is distributed over the societal network of a given VW/MMOG. Representing, manipulating, and inferring trust from the user/player point of view certainly is a grand challenge. When someone meets an unknown individual, the question is “Can I trust him/her or not?”. It is clear that this requires the user to have access to a representation of trust about others, but, unless we are using an open source VW/MMOG, it is difficult —not to say unfeasible— to get access to such data. Even, in an open source system, a number of users may refuse to pass information about its friends, acquaintances, or others. Putting together its own data and gathered data obtained from others, the avatar-impersonated player should be able to come across a trust result about its current trustee. For the trust assessment method used in this thesis, we use subjective logic operators and graph search algorithms to undertake such trust inference about the trustee. The proposed trust inference system has been validated using a number of OpenSimulator (opensimulator.org) scenarios, which showed an accuracy increase in evaluating trustability of avatars. Summing up, our proposal aims thus to introduce a trust theory for virtual worlds, its trust assessment metrics (e.g., subjective logic) and trust discovery methods (e.g., graph search methods), on an individual basis, rather than based on usual centralized reputation systems. In particular, and unlike other trust discovery methods, our methods run at interactive rates.MMOGs (Massively Multiplayer Online Games, como por exemplo, World of Warcraft), mundos virtuais (VW, como por exemplo, o Second Life) e redes sociais (como por exemplo, Facebook) necessitam de mecanismos de confiança mais autónomos, capazes de assegurar a segurança e a confiança de uma forma semelhante à que os seres humanos utilizam na vida real. Como se sabe, esta não é uma questão fácil. Porque confiar em seres humanos e ou organizações depende da percepção e da experiência de cada indivíduo, o que é difícil de quantificar ou medir à partida. Na verdade, esses ambientes sociais carecem dos mecanismos de confiança presentes em interacções humanas presenciais. Além disso, as interacções mediadas por dispositivos computacionais estão em constante evolução, necessitando de mecanismos de confiança adequados ao ritmo da evolução para avaliar situações de risco. Em VW/MMOGs, é amplamente reconhecido que os utilizadores desenvolvem relações de confiança a partir das suas interacções no mundo com outros. No entanto, essas relações de confiança acabam por não ser representadas nas estruturas de dados (ou bases de dados) do VW/MMOG específico, embora às vezes apareçam associados à reputação e a sistemas de reputação. Além disso, tanto quanto sabemos, ao utilizador não lhe é facultado nenhum mecanismo que suporte uma ferramenta de confiança individual para sustentar o seu processo de tomada de decisão, enquanto ele interage com outros utilizadores no mundo virtual ou jogo. A fim de resolver este problema, bem como os mencionados acima, propomos nesta tese uma representação formal para essas relações de confiança pessoal, baseada em interacções avatar-avatar. A ideia principal é fornecer a cada jogador representado por um avatar uma ferramenta de confiança pessoal que segue um modelo de confiança distribuída, ou seja, os dados de confiança são distribuídos através da rede social de um determinado VW/MMOG. Representar, manipular e inferir a confiança do ponto de utilizador/jogador, é certamente um grande desafio. Quando alguém encontra um indivíduo desconhecido, a pergunta é “Posso confiar ou não nele?”. É claro que isto requer que o utilizador tenha acesso a uma representação de confiança sobre os outros, mas, a menos que possamos usar uma plataforma VW/MMOG de código aberto, é difícil — para não dizer impossível — obter acesso aos dados gerados pelos utilizadores. Mesmo em sistemas de código aberto, um número de utilizadores pode recusar partilhar informações sobre seus amigos, conhecidos, ou sobre outros. Ao juntar seus próprios dados com os dados obtidos de outros, o utilizador/jogador representado por um avatar deve ser capaz de produzir uma avaliação de confiança sobre o utilizador/jogador com o qual se encontra a interagir. Relativamente ao método de avaliação de confiança empregue nesta tese, utilizamos lógica subjectiva para a representação da confiança, e também operadores lógicos da lógica subjectiva juntamente com algoritmos de procura em grafos para empreender o processo de inferência da confiança relativamente a outro utilizador. O sistema de inferência de confiança proposto foi validado através de um número de cenários Open-Simulator (opensimulator.org), que mostrou um aumento na precisão na avaliação da confiança de avatares. Resumindo, a nossa proposta visa, assim, introduzir uma teoria de confiança para mundos virtuais, conjuntamente com métricas de avaliação de confiança (por exemplo, a lógica subjectiva) e em métodos de procura de caminhos de confiança (com por exemplo, através de métodos de pesquisa em grafos), partindo de uma base individual, em vez de se basear em sistemas habituais de reputação centralizados. Em particular, e ao contrário de outros métodos de determinação do grau de confiança, os nossos métodos são executados em tempo real