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19
SybilInfer: Detecting Sybil Nodes using Social Networks
"... SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference ..."
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Cited by 62 (5 self)
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SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and realworld network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results. 1
BotGrep: Finding P2P Bots with Structured Graph Analysis
"... A key feature that distinguishes modern botnets from earlier counterparts is their increasing use of structured overlay topologies. This lets them carry out sophisticated coordinated activities while being resilient to churn, but it can also be used as a point of detection. In this work, we devise t ..."
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Cited by 21 (1 self)
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A key feature that distinguishes modern botnets from earlier counterparts is their increasing use of structured overlay topologies. This lets them carry out sophisticated coordinated activities while being resilient to churn, but it can also be used as a point of detection. In this work, we devise techniques to localize botnet members based on the unique communication patterns arising from their overlay topologies used for command and control. Experimental results on synthetic topologies embedded within Internet traffic traces from an ISP’s backbone network indicate that our techniques (i) can localize the majority of bots with low false positive rate, and (ii) are resilient to incomplete visibility arising from partial deployment of monitoring systems and measurement inaccuracies from dynamics of background traffic. 1
Mathematical aspects of mixing times in markov chains
 FOUND. TRENDS THEOR. COMPUT. SCI
, 2006
"... ..."
Anonymity in the wild: Mixes on unstructured networks
 In Nikita Borisov and Philippe Golle, editors, Privacy Enhancing Technologies
, 2007
"... Abstract. As decentralized computing scenarios get ever more popular, unstructured topologies are natural candidates to consider running mix networks upon. We consider mix network topologies where mixes are placed on the nodes of an unstructured network, such as social networks and scalefree random ..."
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Cited by 12 (3 self)
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Abstract. As decentralized computing scenarios get ever more popular, unstructured topologies are natural candidates to consider running mix networks upon. We consider mix network topologies where mixes are placed on the nodes of an unstructured network, such as social networks and scalefree random networks. We explore the efficiency and traffic analysis resistance properties of mix networks based on unstructured topologies as opposed to theoretically optimal structured topologies, under high latency conditions. We consider a mix of directed and undirected network models, as well as one real world case study – the LiveJournal friendship network topology. Our analysis indicates that mixnetworks based on scalefree and smallworld topologies have, firstly, mixroute lengths that are roughly comparable to those in expander graphs; second, that compromise of the most central nodes has little effect on anonymization properties, and third, batch sizes required for warding off intersection attacks need to be an order of magnitude higher in unstructured networks in comparison with expander graph topologies. 1
Formal Analysis Techniques for Gossiping Protocols
 ACM SIGOPS Oper. Syst. Rev.
, 2007
"... We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict the ..."
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Cited by 11 (4 self)
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We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them.
Formal Concept Sampling for Counting and ThresholdFree Local Pattern Mining
"... We describe a MetropolisHastings algorithm for sampling formal concepts, i.e., closed (item) sets, according to any desired strictly positive distribution. Important applications are (a) estimating the number of all formal concepts as well as (b) discovering any number of interesting, nonredundan ..."
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Cited by 4 (1 self)
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We describe a MetropolisHastings algorithm for sampling formal concepts, i.e., closed (item) sets, according to any desired strictly positive distribution. Important applications are (a) estimating the number of all formal concepts as well as (b) discovering any number of interesting, nonredundant, and representative local patterns. Setting (a) can be used for estimating the runtime of algorithms examining all formal concepts. An application of setting (b) is the construction of data mining systems that do not require any userspecified threshold like minimum frequency or confidence. 1
Brief Announcement: Performance Analysis of Cyclon, an Inexpensive Membership Management for Unstructured 49 Overlays
 In DISC 2006
, 2006
"... Motivations. Unstructured overlays form an important class of peertopeer networks, notably for contentbased searching algorithms. Being able to build overlays withlow diameter, thatareresilienttounpredictablejoins andleaves, in a totally distributed manner is a challenging task. Random graphs exh ..."
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Cited by 3 (0 self)
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Motivations. Unstructured overlays form an important class of peertopeer networks, notably for contentbased searching algorithms. Being able to build overlays withlow diameter, thatareresilienttounpredictablejoins andleaves, in a totally distributed manner is a challenging task. Random graphs exhibit such properties, and have been extensively studied in literature. Cyclon algorithm is an inexpensive gossipbased membership management protocol described in detail in [1] that meets these requirements. An Overview of Cyclon. For a detailed description of Cyclon algorithm, the reader should refer to [1]. Briefly, Cyclon supports two different modes of operation: a basic shuffling mode, and an enhanced one. The basic mode, the only one to be studied in this article is a purely random mode, while the second mode uses a timestamp mechanism to improve performance with respect to node failures behavior. Each node maintains a cache of neighbor nodes of size c, hence each node knows exactly c nodes in the overlay. To correctly initialize nodes caches, we assume the existence of a predefined set of wellknown supernodes. During the execution of the protocol, each node performs periodically a shuffle
Intelligent Pattern Mining via Quick Parameter Evaluation (Extended Abstract)
"... When embedded in a larger workflow, pattern mining algorithms can be required to produce huge outputs. In this situation the usual trialanderror parameter twiddling is infeasible, because every mining run consumes a considerable amount of time. Instead an intelligent mining process is needed that ..."
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Cited by 1 (0 self)
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When embedded in a larger workflow, pattern mining algorithms can be required to produce huge outputs. In this situation the usual trialanderror parameter twiddling is infeasible, because every mining run consumes a considerable amount of time. Instead an intelligent mining process is needed that is able to provide good parameter choices on its own. To realize this vision, quick parameter evaluation algorithms are needed that can give insights into the size and structure of a pattern mining’s result set before the actual mining is started. The design of such procedures is a challenging algorithmic problem. As a first step towards a possible solution we empirically evaluate an exemplary implementation of a sampling approach.