Towards Securing Peer-to-peer SIP in the MANET Context: Existing Work and Perspectives

The Session Initiation Protocol (SIP) is a key building block of many social applications, including VoIP communication and instant messaging. In its original architecture, SIP heavily relies on servers such as proxies and registrars. Mobile Ad hoc NETworks (MANETs) are networks comprised of mobile devices that communicate over wireless links, such as tactical radio networks or vehicular networks. In such networks, no fixed infrastructure exists and server-based solutions need to be redesigned to work in a peer-to-peer fashion. We survey existing proposals for the implementation of SIP over such MANETs and analyze their security issues. We then discuss potential solutions and their suitability in the MANET context

Distributed Wikis: A Survey

International audienceSUMMARY "Distributed Wiki" is a generic term covering various systems, including "peer-to-peer wiki," "mobile wiki," "offline wiki," "federated wiki" and others. Distributed wikis distribute their pages among the sites of autonomous participants to address various motivations, including high availability of data, new collaboration models and different viewpoint of subjects. Although existing systems share some common basic concepts, it is often difficult to understand the specificity of each one, the underlying complexities or the best context in which to use it. In this paper, we define, classify and characterize distributed wikis. We identify three classes of distributed wiki systems, each using a different collaboration model and distribution scheme for its pages: highly available wikis, decentralized social wikis and federated wikis. We classify existing distributed wikis according to these classes. We detail their underlying complexities and social and technical motivations. We also highlight some directions for research and opportunities for new systems with original social and technical motivations

Adaptive Filtering of Advertisements on Web Pages

We present a browser extension to dynamically learn to filter unwanted images (such as advertisements or flashy graphics) based on minimal user feedback. To do so, we apply the weighted majority algorithm using pieces of the Uniform Resource Locators of such images as predictors. Experimental results tend to confirm that the accuracy of the predictions converges quickly to very high levels