Automated Hubs-Patching: Protection Against Malware Spread Through Reduced Scale-Free Networks and External Storage Devices

Patches can be distributed through technological networks at a high rate, targeting a sufficient fraction of nodes. This selective and rapid automated process not only prevents and contains the negative spread of malware but also minimizes costs and avoids network congestion. We propose a novel epidemic model to study the dynamics of patching highly linked nodes (hubs) as a preventive measure against malware spreading through the Internet and removable storage devices. We analytically prove the model's two endemic equilibria and their global stabilities. Numerical simulations are performed to validate this analysis and explore the influence of different parameters on the asymptotic fraction of infected and patched nodes. Our new findings show that (i) Barabási's network is more conducive to inhibiting computer viruses spread than other scale-free networks, (ii) it is possible to control and reduce the spread of a rapid and large-scale viral attack targeting hubs by keeping the ratio of the patch failure rate and the patch forwarding rate below a threshold that characterizes the network, and by implementing other specific countermeasures at lowly linked nodes level. Our approach provides insights into the dynamics of effective patch dissemination and identifies strategies to combat malware spread.