Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

Abstract

As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q<qc. The results showed that our model is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks

“…The decomposed Markov processes deduced a widely approved result that a virus dies out quickly if / < 1/ 1 (A), where A is the adjacency matrix of the network, and 1 (A) is the largest eigenvalue of A. The network can also be dynamic, where nodes can transfer among communities [21]. The simulation results on two communities revealed that the node mobility can accelerate the malware propagation and improve the epidemic threshold.…”

“…where J 11 is an 2 × 2 diagonal matrix whose diagonal entries are − . We note that J 21 and J 31 are zero matrices. As a result, J is an upper block triangular matrix, and…”

Dynamics of Botnet Propagation in Software Defined Networks Using Epidemic Models

“…Where they proposed a coloring algorithm to limit the ability of a malicious controller to compromise its neighboring controllers. Lui et al [40] presented a SIS dynamic model with a time-varying community network to analyse the spreading processes of malware in SDN. However, these models did not consider the fact that the controllers can also infect the forwarding devices as we do in our models.…”

Teaching software‐defined network security through malicious tenant detection

“…The output of our method is a Malicious Tenant List. A response module can manage and quarantine malicious tenants in the entire network by changing the flow table strategy . Figure depicts the steps and Figure illustrates the components of the proposed approach.…”