Gilles Trédan

A GENERIC TRUST FRAMEWORK FOR LARGE-SCALE OPEN SYSTEMS USING MACHINE LEARNING

In many large‐scale distributed systems and on the Web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable interaction environment. A traditional approach to reason about the risk of a transaction is to determine if the involved agent is trustworthy on the basis of its behavior history. As a departure from such traditional trust models, we propose a generic, trust framework based on machine learning where an agent uses its own previous transactions (with other agents) to build a personal knowledge base. This is used to assess the trustworthiness of a transaction on the basis of the associated features, particularly using the features that help discern successful transactions from unsuccessful ones. These features are handled by applying appropriate machine learning algorithms to extract the relationships between the potential transaction and the previous ones. Experiments based on real data sets show that our approach is more accurate than other trust mechanisms, especially when the information about past behavior of the specific agent is rare, incomplete, or inaccurate.

Adversarial VNet embeddings: A threat for ISPs?

This paper demonstrates that virtual networks that are dynamically embedded on a given resource network may constitute a security threat as properties of the infrastructure-typically a business secret-are disclosed. We initiate the study of this new problem and introduce the notion of request complexity which captures the number of virtual network embedding requests needed to fully disclose the infrastructure topology. We derive lower bounds and present algorithms achieving an asymptotically optimal request complexity for the important class of tree and cactus graphs (complexity θ(n)) as well as arbitrary graphs (complexity θ(n2)).

SOUK: social observation of human kinetics

Simulating human-centered pervasive systems requires accurate assumptions on the behavior of human groups. Recent models consider this behavior as a combination of both social and spatial factors. Yet, establishing accurate traces of human groups is difficult: current techniques capture either positions, or contacts, with a limited accuracy. In this paper we introduce a new technique to capture such behaviors. The interest of this approach lies in the unprecedented accuracy at which both positions and orientations of humans, even gathered in a crowd, are captured. From the mobility to the topological connectivity, the open-source framework we developed offers a layered approach that can be tailored, allowing to compare and reason about models and traces. We introduce a new trace of 50 individuals on which the validity and accuracy of this approach is demonstrated. To showcase the interest of our software pipeline, we compare it against the random waypoint model. Our fine-grained analyses, that take into account social interactions between users, show that the random waypoint model is not a reasonable approximation of any of the phenomena we observed.

Modeling and measuring graph similarity: the case for centrality distance

The study of the topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity. This paper proposes a natural similarity measure for complex networks: centrality distance, the difference between two graphs with respect to a given node centrality. Centrality distances allow to take into account the specific roles of the different nodes in the network, and have many interesting applications. As a case study, we consider the closeness centrality in more detail, and show that closeness centrality distance can be used to effectively distinguish between randomly generated and actual evolutionary paths of two dynamic social networks.

Heuristical Top-k: Fast Estimation of Centralities in Complex Networks

Abstract Centrality metrics have proven to be of a major interest when analyzing the structure of networks. Given modern-day network sizes, fast algorithms for estimating these metrics are needed. This paper proposes a computation framework (named Filter-Compute-Extract) that returns an estimate of the top- k most important nodes in a given network. We show that considerable savings in computation time can be achieved by first filtering the input network based on correlations between cheap and more costly centrality metrics. Running the costly metric on the smaller resulting filtered network yields significant gains in computation time. We examine the complexity improvement due to this heuristic for classic centrality measures, as well as experimental results on well-studied public networks.

Misleading stars: what cannot be measured in the Internet?

Traceroute measurements are one of the main instruments to shed light onto the structure and properties of today’s complex networks such as the Internet. This article studies the feasibility and infeasibility of inferring the network topology given traceroute data from a worst-case perspective, i.e., without any probabilistic assumptions on, e.g., the nodes’ degree distribution. We attend to a scenario where some of the routers are anonymous, and propose two fundamental axioms that model two basic assumptions on the traceroute data: (1) each trace corresponds to a real path in the network, and (2) the routing paths are at most a factor

Adversarial topology discovery in network virtualization environments: a threat for ISPs?

1/\alpha

Tomographic Node Placement Strategies and the Impact of the Routing Model

off the shortest paths, for some parameter