Biblio

The heterogeneous SIS model for virus spread in any finite size graph characterizes the influence of factors of SIS model and could be analyzed by the extended N-Intertwined model introduced in [1]. We specifically focus on the heterogeneous virus spread in the star network in this paper. The epidemic threshold and the average meta-stable state fraction of infected nodes are derived for virus spread in the star network. Our results illustrate the effect of the factors of SIS model on the steady state infection.

Techniques for network security analysis have historically focused on the actions of the network hosts. Outside of forensic analysis, little has been done to detect or predict malicious or infected nodes strictly based on their association with other known malicious nodes. This methodology is highly prevalent in the graph analytics world, however, and is referred to as community detection. In this paper, we present a method for detecting malicious and infected nodes on both monitored networks and the external Internet. We leverage prior community detection and graphical modeling work by propagating threat probabilities across network nodes, given an initial set of known malicious nodes. We enhance prior work by employing constraints that remove the adverse effect of cyclic propagation that is a byproduct of current methods. We demonstrate the effectiveness of probabilistic threat propagation on the tasks of detecting botnets and malicious web destinations.