A Mobility-Based Epidemic Model for IoT Malware Spread

Abstract

With the rapid advancement of technology, IoT has become inseparable from human lives. IoT is extensively used in transport, healthcare, manufacturing, among other sectors. However, this technology lacks sufficient security defense capabilities, thus becoming a highway for malicious actors. IoT networks use infrastructure-based (INF) and device-to-device (D2D) communications to propagate data. The INF communication utilizes technologies such as WLAN, LTE, GPRS, and GSM to relay information from source to destination. The D2D paradigm, on the other hand, is a close-proximity communication in which sensors exchange data in a multi-hop manner. Since malware can utilize both D2D and INF links to spread out, IoT networks are exceptionally vulnerable to attacks. Therefore, we propose Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model to examine the dynamics of IoT malware spread via INF and D2D communications.We analyze the impacts of mobility on infection propagation and illustrate that our model adequately captures IoT malware spread behaviors through mathematical analysis and simulations. We also compute the malware transmission threshold, which can be used as a guideline to mitigate and suppress an attack.

Deriving Exact Mathematical Models of Malware Based on Random Propagation

“…All malware mathematical models are based on a mix of these common states, developing specific transitions between them according to the modeled network. Examples can be found in SIRS models [22,23], SEIRS models [24][25][26], or SEIRD models [27,28]), to cite the most popular combinations. On top of them, innumerable variations introduce one or more new subdynamics, e.g., [22], where an SIRS-L model was proposed to account for the low-energy mode of wireless sensors.…”

“…Moreover, the diversity of software platforms on which Internet connectivity is built marks IoT networks as strongly heterogeneous compared to former networks [30], increasing surfaces of attack. This complexity can be introduced in models by adding more parameters and elements to the transition between states, like the model of [28] did to model heterogeneous IoT networks where malware spreads both via standard network infrastructure and device-to-device connections (Equations (3a)-(3e)):…”