Preventing Malware Pandemics in Mobile Devices by Establishing Response-time Bounds

TL;DR

This paper models malware spread among mobile devices in a city, establishing response-time bounds for counter-measures to prevent a pandemic, supported by simulations and experimental results.

Contribution

It introduces a combined malware propagation and mobility model, and develops a simulator to analyze response-time bounds for pandemic prevention in mobile networks.

Findings

Response-time bounds effectively prevent malware spread.

Simulation results validate the model's predictions.

Timely counter-measures significantly reduce infection rates.

Abstract

We study the propagation of a malicious software in a network of mobile devices, which are moving in a specific city area, and establish time bounds for the activation of a counter-measure, i.e., an antivirus or a cleaner in order to prevent pandemic. More precisely, given an initial infected population (mobile devices), we establish upper bounds on the time needed for a counter-measure to take effect after infection (response-time), in order to prevent the rest susceptible devices to get infected. Thus, within a period of time, we guarantee that not all the susceptible devices in the city get infected and the infected ones get sanitized. In our work, we first propose a malware propagation model along with a device mobility model and then, utilizing these models, we develop a simulator that we use to study the spread of malware in such networks. Finally, we provide experimental results for the pandemic prevention taken by our simulator for various response-time intervals.