Optimal Control of Malware Propagation in IoT Networks

TL;DR

This paper develops an optimal control strategy to mitigate malware spread in IoT networks, especially in smart homes, by modeling infection dynamics and analyzing intervention impacts through simulations.

Contribution

It introduces a novel node-based epidemiological model for IoT malware spread and formulates an optimal control approach to minimize infections before patch deployment.

Findings

The model accurately captures malware propagation dynamics.

Optimal control reduces infected devices significantly.

Simulations demonstrate effectiveness in real-world scenarios.

Abstract

The rapid proliferation of Internet of Things (IoT) devices in recent years has resulted in a significant surge in the number of cyber-attacks targeting these devices. Recent data indicates that the number of such attacks has increased by over 100 percent, highlighting the urgent need for robust cybersecurity measures to mitigate these threats. In addition, a cyber-attack will begin to spread malware across the network once it has successfully compromised an IoT network. However, to mitigate this attack, a new patch must be applied immediately. In reality, the time required to prepare and apply the new patch can vary significantly depending on the nature of the cyber-attack. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home. A novel node-based epidemiological model susceptible, infected high, infected low, recover first, and recover complete(SI_HI_LR_FR_C) is established with immediate response state for the restricted environment. After that, the impact of malware on IoT devices using both high and low infected rates will be analyzed. Finally, to illustrate the main results, several numerical analyses are carried out in addition to simulate the real-world scenario of IoT networks in the smart home, we built a dataset to be used in the experiments.