Fuzzy decision support for service selection in e-business environments

The emergence of semantic overlay networks as instruments to improve security, trust and stability in distributed virtual communities is recognized widely in the research community. We propose a fuzzy logic based framework which integrates social information such as trustworthiness, reputation and credibility ratings for individuals, alliances, organizations, services and products in e-commerce markets. This framework is designed to support the decision making process of autonomous agents during the selection of the optimal business partner. Fuzzy systems provide the ideal capabilities to process multiple criteria, which are composed of imprecise information and attribute definitions expressed in natural language. The proposed fuzzy models implement the DEco Arch framework and ontologies which provide details about concepts and their relationships in virtual communitie

Supporting cooperation and coordination in open multi-agent systems

Cooperation and coordination between agents are fundamental processes for increasing aggregate and individual benefit in open Multi-Agent Systems (MAS). The increased ubiquity, size, and complexity of open MAS in the modern world has prompted significant research interest in the mechanisms that underlie cooperative and coordinated behaviour. In open MAS, in which agents join and leave freely, we can assume the following properties: (i) there are no centralised authorities, (ii) agent authority is uniform, (iii) agents may be heterogeneously owned and designed, and may consequently have con icting intentions and inconsistent capabilities, and (iv) agents are constrained in interactions by a complex connecting network topology. Developing mechanisms to support cooperative and coordinated behaviour that remain effective under these assumptions remains an open research problem. Two of the major mechanisms by which cooperative and coordinated behaviour can be achieved are (i) trust and reputation, and (ii) norms and conventions. Trust and reputation, which support cooperative and coordinated behaviour through notions of reciprocity, are effective in protecting agents from malicious or selfish individuals, but their capabilities can be affected by a lack of information about potential partners and the impact of the underlying network structure. Regarding conventions and norms, there are still a wide variety of open research problems, including: (i) manipulating which convention or norm a population adopts, (ii) how to exploit knowledge of the underlying network structure to improve mechanism efficacy, and (iii) how conventions might be manipulated in the middle and latter stages of their lifecycle, when they have become established and stable. In this thesis, we address these issues and propose a number of techniques and theoretical advancements that help ensure the robustness and efficiency of these mechanisms in the context of open MAS, and demonstrate new techniques for manipulating convention emergence in large, distributed populations. Specfically, we (i) show that gossiping of reputation information can mitigate the detrimental effects of incomplete information on trust and reputation and reduce the impact of network structure, (ii) propose a new model of conventions that accounts for limitations in existing theories, (iii) show how to manipulate convention emergence using small groups of agents inserted by interested parties, (iv) demonstrate how to learn which locations in a network have the greatest capacity to in uence which convention a population adopts, and (v) show how conventions can be manipulated in the middle and latter stages of the convention lifecycle

Novel Analytical Modelling-based Simulation of Worm Propagation in Unstructured Peer-to-Peer Networks

Millions of users world-wide are sharing content using Peer-to-Peer (P2P) networks, such as Skype and Bit Torrent. While such new innovations undoubtedly bring benefits, there are nevertheless some associated threats. One of the main hazards is that P2P worms can penetrate the network, even from a single node and then spread rapidly. Understanding the propagation process of such worms has always been a challenge for researchers. Different techniques, such as simulations and analytical models, have been adopted in the literature. While simulations provide results for specific input parameter values, analytical models are rather more general and potentially cover the whole spectrum of given parameter values. Many attempts have been made to model the worm propagation process in P2P networks. However, the reported analytical models to-date have failed to cover the whole spectrum of all relevant parameters and have therefore resulted in high false-positives. This consequently affects the immunization and mitigation strategies that are adopted to cope with an outbreak of worms. The first key contribution of this thesis is the development of a susceptible, exposed, infectious, and Recovered (SEIR) analytical model for the worm propagation process in a P2P network, taking into account different factors such as the configuration diversity of nodes, user behaviour and the infection time-lag. These factors have not been considered in an integrated form previously and have been either ignored or partially addressed in state-of-the-art analytical models. Our proposed SEIR analytical model holistically integrates, for the first time, these key factors in order to capture a more realistic representation of the whole worm propagation process. The second key contribution is the extension of the proposed SEIR model to the mobile M-SEIR model by investigating and incorporating the role of node mobility, the size of the worm and the bandwidth of wireless links in the worm propagation process in mobile P2P networks. The model was designed to be flexible and applicable to both wired and wireless nodes. The third contribution is the exploitation of a promising modelling paradigm, Agent-based Modelling (ABM), in the P2P worm modelling context. Specifically, to exploit the synergies between ABM and P2P, an integrated ABM-Based worm propagation model has been built and trialled in this research for the first time. The introduced model combines the implementation of common, complex P2P protocols, such as Gnutella and GIA, along with the aforementioned analytical models. Moreover, a comparative evaluation between ABM and conventional modelling tools has been carried out, to demonstrate the key benefits of ease of real-time analysis and visualisation. As a fourth contribution, the research was further extended by utilizing the proposed SEIR model to examine and evaluate a real-world data set on one of the most recent worms, namely, the Conficker worm. Verification of the model was achieved using ABM and conventional tools and by then comparing the results on the same data set with those derived from developed benchmark models. Finally, the research concludes that the worm propagation process is to a great extent affected by different factors such as configuration diversity, user-behaviour, the infection time lag and the mobility of nodes. It was found that the infection propagation values derived from state-of-the-art mathematical models are hypothetical and do not actually reflect real-world values. In summary, our comparative research study has shown that infection propagation can be reduced due to the natural immunity against worms that can be provided by a holistic exploitation of the range of factors proposed in this work

Damage Detection and Mitigation in Open Collaboration Applications

Collaborative functionality is changing the way information is amassed, refined, and disseminated in online environments. A subclass of these systems characterized by open collaboration uniquely allow participants to *modify* content with low barriers-to-entry. A prominent example and our case study, English Wikipedia, exemplifies the vulnerabilities: 7%+ of its edits are blatantly unconstructive. Our measurement studies show this damage manifests in novel socio-technical forms, limiting the effectiveness of computational detection strategies from related domains. In turn this has made much mitigation the responsibility of a poorly organized and ill-routed human workforce. We aim to improve all facets of this incident response workflow. Complementing language based solutions we first develop content agnostic predictors of damage. We implicitly glean reputations for system entities and overcome sparse behavioral histories with a spatial reputation model combining evidence from multiple granularity. We also identify simple yet indicative metadata features that capture participatory dynamics and content maturation. When brought to bear over damage corpora our contributions: (1) advance benchmarks over a broad set of security issues ( vandalism ), (2) perform well in the first anti-spam specific approach, and (3) demonstrate their portability over diverse open collaboration use cases. Probabilities generated by our classifiers can also intelligently route human assets using prioritization schemes optimized for capture rate or impact minimization. Organizational primitives are introduced that improve workforce efficiency. The whole of these strategies are then implemented into a tool ( STiki ) that has been used to revert 350,000+ damaging instances from Wikipedia. These uses are analyzed to learn about human aspects of the edit review process, properties including scalability, motivation, and latency. Finally, we conclude by measuring practical impacts of work, discussing how to better integrate our solutions, and revealing outstanding vulnerabilities that speak to research challenges for open collaboration security

Providing incentive to peer-to-peer applications

Cooperative peer-to-peer applications are designed to share the resources of participating computers for the common good of ail users. However, users do not necessarily have an incentive to donate resources to the system if they can use the system's resources for free. As commonly observed in deployed applications, this situation adversely affects the applications' performance and sometimes even their availability and usability. While traditional resource management is handled by a centralized enforcement entity, adopting similar solution raises new concerns for distributed peer-to-peer systems. This dissertation proposes to solve the incentive problem in peer-to-peer applications by designing fair sharing policies and enforcing these policies in a distributed manner. The feasibility and practicability of this approach is demonstrated through numerous applications, namely archival storage systems, streaming systems, content distribution systems, and anonymous communication systems

Trust-Aware Peer Sampling: Performance and Privacy Tradeoffs

International audienceThe ability to identify people that share one's own interests is one of the most interesting promises of the Web 2.0 driving user-centric applications such as recommendation systems or collaborative marketplaces. To be truly useful, however, information about other users also needs to be associated with some notion of trust. Consider a user wishing to sell a concert ticket. Not only must she find someone who is interested in the concert, but she must also make sure she can trust this person to pay for it. This paper addresses the need for trust in user-centric applications by propos- ing two novel distributed protocols that combine interest-based connections be- tween users with explicit links obtained from social networks à-la Facebook. Both protocols build trusted multi-hop paths between users in an explicit so- cial network supporting the creation of semantic overlays backed up by social trust. The first protocol, TAPS2 , extends our previous work on TAPS (Trust- Aware Peer Sampling), by improving the ability to locate trusted nodes. Yet, it remains vulnerable to attackers wishing to learn about trust values between ar- bitrary pairs of users. The second protocol, PTAPS (Private TAPS ), improves TAPS2 with provable privacy guarantees by preventing users from revealing their friendship links to users that are more than two hops away in the social network. In addition to proving this privacy property, we evaluate the per- formance of our protocols through event-based simulations, showing significant improvements over the state of the art

Models and applications for the Bitcoin ecosystem

Cryptocurrencies are widely known and used principally as a means of investment and payment by more and more users outside the restricted circle of technologists and computer scientists. However, like fiat money, they can also be used as a means for illegal activities, exploiting their pseudo-anonymity and easiness/speed in moving capitals. This thesis aims to provide a suite of tools and models to better analyze and understand several aspect of the Bitcoin blockchain. In particular, we developed a visual tool that highlights transaction islands, i.e., the sub-graphs disconnected from the super-graph, which represents the whole blockchain. We also show the distributions of Bitcoin transactions types and define new classes of nonstandard transactions. We analyze the addresses reuse in Bitcoin, showing that it corresponds to malicious activities in the Bitcoin ecosystem. Then we investigate whether solids or weak forms of arbitrage strategies are possible by trading across different Bitcoin Exchanges. We found that Bitcoin price/exchange rate is influenced by future and past events. Finally, we present a Stochastic Model to quantitative analyze different consensus protocols. In particular, the probabilistic analysis of the Bitcoin model highlights how forks happen and how they depend on specific parameters of the protocol