Spatial-Temporal Modeling of Malware Propagation in Networks

Overview

Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this paper is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. The model is motivated by probabilistic graphs, which have been widely investigated in machine learning. the authors first use a graphical representation to abstract the propagation of malwares that employ different scanning methods and then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies.