Mobile ad hoc networks have inherently different properties than traditional wired networks. These new characteristics present different security vulnerabilities and this paper provides a detailed classification of these threats. Threats exist to a mobile ad hoc network both from external nodes unauthorized to participate in the mobile ad hoc networks, and from internal nodes, which have the authorization credentials to participate in the mobile ad hoc network. Internal nodes giving rise to threats can be further divided according to their behavior-failed, badly failed, selfish and malicious nodes. All categories of node behavior should be considered when designing protocols for mobile ad hoc networks.

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The clustering coefficient has been introduced to capture the social phenomena that a friend of a friend tends to be my friend. This metric has been widely studied and has shown to be of great interest to describe the characteristics of a social graph. But, the clustering coefficient is originally defined for a graph in which the links are undirected, such as friendship links (Facebook) or professional links (LinkedIn). For a graph in which links are directed from a source of information to a consumer of information, it is no more adequate. We show that former studies have missed much of the information contained in the directed part of such graphs. In this article, we introduce a new metric to measure the clustering of directed social graphs with interest links, namely the interest clustering coefficient. We compute it (exactly and using sampling methods) on a very large social graph, a Twitter snapshot with 505 million users and 23 billion links, as well as other various datasets. We additionally provide the values of the formerly introduced directed and undirected metrics, a first on such a large snapshot. We observe a higher value of the interest clustering coefficient than classic directed clustering coefficients, showing the importance of this metric. By studying the bidirectional edges of the Twitter graph, we also show that the interest clustering coefficient is more adequate to capture the interest part of the graph while classic ones are more adequate to capture the social part. We also introduce a new model able to build random networks with a high value of interest clustering coefficient. We finally discuss the interest of this new metric for link recommendation.