The seven features used in this study are in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality.
In this study, we propose a novel botnet detection methodology based on topological features of nodes within a graph: in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality.
We investigate seven graphed-based features that are may be connected to bot activities: in degree, out degree, in degree weight, out degree weight, clustering coefficient, node betweenness, and eigenvector centrality.
Out degree weight and can be described as the total number of data packets sent by a particular node to its neighboring connected nodes.
In degree weight refers to the total number of data packets received by a particular node transferred from its neighboring connected nodes.
It remains unclear, however, whether and to what degree weight gain between deliveries (ie, interdelivery weight gain) is associated with cesarean delivery in a subsequent pregnancy following a vaginal delivery.
To measure the convergence, we have successively calculated the distance between the rescaled degree (weight) distributions of networks aggregated over an interval of length t and networks aggregated over twice longer intervals of length 2t.
Same as in degree weight, we assume that bots will have similarity in the volume of data it sends out to other IP addresses in the network and can be a useful indicator of botnet activity in a network.
The third weight policy ('inflated compound degree weight') takes the square of the node weights defined by the second weight policy.
The parameter values having highest impact on the pathway inference accuracy are in this order: compound degree weight and inflated compound degree weight outperform unit weight, directed network outperforms undirected network, kWalks supersedes hybrid approaches and three kWalk iterations are better than a single run.
