Zhaikhan, Ainur, Kishk, Mustafa A., ElSawy, Hesham and Alouini, Mohamed-Slim (2021) Safeguarding the IoT From Malware Epidemics: A Percolation Theory Approach. IEEE Internet of Things Journal, 8 (7). pp. 6039-6052. ISSN 2372-2541
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Abstract
The upcoming Internet of Things (IoT) is foreseen to
encompass massive numbers of connected devices, smart objects,
and cyber–physical systems. Due to the large scale and massive deployment of devices, it is deemed infeasible to safeguard
100% of the devices with state-of-the-art security countermeasures. Hence, large-scale IoT has inevitable loopholes for network
intrusion and malware infiltration. Even worse, exploiting the
high density of devices and direct wireless connectivity, malware
infection can stealthily propagate through susceptible (i.e., unsecured) devices and form an epidemic outbreak without being
noticed to security administration. A malware outbreak enables
adversaries to compromise a large population of devices, which
can be exploited to launch versatile cyber and physical malicious attacks. In this context, we utilize spatial firewalls, to
safeguard the IoT from malware outbreak. In particular, spatial firewalls are computationally capable devices equipped with
state-of-the-art security and anti-malware programs that are spatially deployed across the network to filter the wireless traffic in
order to detect and thwart malware propagation. Using tools
from percolation theory, we prove that there exists a critical
density of spatial firewalls beyond which malware outbreak is
impossible. This, in turn, safeguards the IoT from malware epidemics regardless of the infection/treatment rates. To this end, a
tractable upper bound for the critical density of spatial firewalls
is obtained. Furthermore, we characterize the relative communications ranges of the spatial firewalls and IoT devices to ensure
secure network connectivity. The percentage of devices secured
by the firewalls is also characterized.
Item Type: | Article |
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Keywords: | Boolean model; network epidemics; percolation theory; random geometric graphs; RGGs; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 16971 |
Identification Number: | 10.1109/JIOT.2020.3034111 |
Depositing User: | Mustafa Kishk |
Date Deposited: | 27 Feb 2023 16:07 |
Journal or Publication Title: | IEEE Internet of Things Journal |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/16971 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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