Abstract. We propose Stegobot, a new generation botnet that communicates over probabilistically unobservable communication channels. It is designed to spread via social malware attacks and steal information from its victims. Unlike conventional botnets, Stegobot traffic does not introduce new communication endpoints between bots. Instead, it is based on a model of covert communication over a social-network overlay -bot to botmaster communication takes place along the edges of a social network. Further, bots use image steganography to hide the presence of communication within image sharing behavior of user interaction. We show that it is possible to design such a botnet even with a less than optimal routing mechanism such as restricted flooding. We analyzed a real-world dataset of image sharing between members of an online social network. Analysis of Stegobot's network throughput indicates that stealthy as it is, it is also functionally powerful -capable of channeling fair quantities of sensitive data from its victims to the botmaster at tens of megabytes every month.

Citizens have always played an important role in emergency management such as urban flooding response. New information and communication technologies such as smartphones and computer-based social networks have great potential to transform the roles of citizens in emergency management. However, current digital citizen science projects are usually limited in three areas: 1) limited one-way citizen participation; 2) no processing and integration of citizens' reports with other existing infrastructure sensing data; 3) no personalized near-real-time spatiotemporal visualization tools for citizens to instantly view aggregated data to gain updated situational awareness. We developed a Mapster application that specifically addresses these issues. First, we leveraged Twitter's geo-referenced tweets functionality to design a customized smartphone application for citizens to report a set of events that have been identified in past urban flooding situations such as "basement flooding" and "powerline down" etc. Second, a Cloud-based semantic streaming data harvesting and processing tool was developed to fetch and process both the Twitter feeds and other infrastructure sensing data such as US National Weather Service's radar data. Third, a user can instantly explore the heterogeneous data processed and provided by the Cloud service through a map-based spatiotemporal animation tool on the smartphone to see how all the events evolve before, during, and after a storm. Such a two-way information flow significantly improves citizen participation and their sense of situational awareness. We present our architecture, implementation, and discussion of issues on citizen science data collection platforms, integration of heterogeneous data sources and future work plan.

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We propose the construction of an unobservable communications network using social networks. The communication endpoints are vertices on a social network. Probabilistically unobservable communication channels are built by leveraging image steganography and the social image sharing behavior of users. All communication takes place along the edges of a social network overlay connecting friends. We show that such a network can provide decent bandwidth even with a far from optimal routing mechanism such as restricted flooding.We show that such a network is indeed usable by constructing a botnet on top of it, called Stegobot. It is designed to spread via social malware attacks and steal information from its victims. Unlike conventional botnets, Stegobot traffic does not introduce new communication endpoints between bots. We analyzed a real-world dataset of image sharing between members of an online social network. Analysis of Stegobot's network throughput indicates that stealthy as it is, it is also functionally powerful -capable of channeling fair quantities of sensitive data from its victims to the botmaster at ens of megabytes every month.