(untitled) (International Journal of Engineering and Computer Science, 2015)
Social network can be generally defined as a group of individuals who are connected by a set of relationships. A key characteristic of social networks is their continual change. However, the bulk of the analysis methods developed and popularized in the field of computer were static in that all information about the time that social interactions take place is discarded. Although recently there is some work on dynamic social network analysis. In this article we have presented the overview of dynamic social grouping algorithm[10][11] . Probabilities and social behaviour are two common criteria used to route a message in disruption/delay tolerant network wherein there is only intermittent connectivity between the nodes. In this article we first discuss how the characteristics of these routing algorithms can be exploited by a malicious node to attract data packets and then dropping them to degrade the network performance. We then show the impact of such a behaviour called blackhole attack on DSG as it leverages both the social behaviour as well as the delivery probabilities to make the forwarding decisions. We present three solutions to mitigate black hole attacks. The first algorithm mitigates non collaborating blackhole nodes. In the second algorithm, we present a solution that handles collaborating blackhole nodes. The first two algorithms handle only the external attacks. It does not handle the scenario in which a node that is good initially and becomes malicious or selfish later. Finally, we present our third algorithm which handles collaborative black holes as well as internal attacks. We validate the performance of our algorithms through extensive simulation in ONE simulator.