Abstract. Content-Centric Networking (CCN) is one of the most promising research area for a future Internet. The goal is to obtain a more scalable, secure, collaborative Internet supporting context-aware services. However, as a new overlay infrastructure, CCN raises the need of a new monitoring architecture to assess security of CCN devices. In particular, the stateful nature of CCN routers introduces new attack threats that need to be addressed. We propose in this paper a monitoring approach for the instrumentation of CCN enabled network nodes. The rationale of our monitoring approach is demonstrated through real experimentations to detect and mitigate network level attacks against CCN.

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People share information with their peers using social media services (e.g. sharing their latest news over Facebook or Twitter) in order to inform the peers about their current situation. This has become a huge part of our social life. During crises this behaviour becomes even more acute because it allows people to reassure their peers (followers and friends) of their well being expeditiously. Of late, social media services have been also used for another purpose during crises: that of informing oneself over the current evolution of the crises. However obtaining relevant information from social media can be a difficult challenge as the bar for posting information, good or bad, is very low. Filtering the flow of messages such that only relevant information is remaining is critical in times of crises. To aid in this, we propose a spatial-temporal model that collects the data from Twitter. The data is further processed to evaluate the density of tweets surrounding the area. We also evaluate the possibility of shared user accounts by determining the physical distance and velocity between messages originating from the same user account.