Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT

With the rapid development of Internet of things technology, the application of intelligent devices in the medical industry has become ubiquitous. Connected devices have revolutionized clinicians and patient care but also made modern hospitals vulnerable to cyber attacks. Among the security risks, b...

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Main Authors: Mingyong Yin, Xingshu Chen, Qixu Wang, Wei Wang, Yulong Wang
Format: Article
Language:English
Published: Hindawi-Wiley 2019-01-01
Series:Security and Communication Networks
Online Access:http://dx.doi.org/10.1155/2019/6803801
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spelling doaj-b9490347ba2e4d6f920698e93c0bb7ef2020-11-25T01:54:35ZengHindawi-WileySecurity and Communication Networks1939-01141939-01222019-01-01201910.1155/2019/68038016803801Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoTMingyong Yin0Xingshu Chen1Qixu Wang2Wei Wang3Yulong Wang4College of Computer Science and Technology, Sichuan University, Chengdu 610065, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCybersecurity Research Institute, Sichuan University, Chengdu 610065, ChinaInstitute of Computer Application, Mianyang 621900, ChinaWith the rapid development of Internet of things technology, the application of intelligent devices in the medical industry has become ubiquitous. Connected devices have revolutionized clinicians and patient care but also made modern hospitals vulnerable to cyber attacks. Among the security risks, botnets are of particular concern, which can be used to control thousands of devices for remote data theft and equipment destruction. In this paper, we propose a non-Markovian spread dynamics model to understand the effects of botnet propagation, which can characterize the hybrid contagion situation in reality. Based on the Susceptible-Adopted-Recovered model, we introduce nonredundant memory spread mechanism for global propagation, as a tuner to adjust spreading rate difference. For describing the proposed model, we extend a heterogeneous edge-based compartmental theory. Through extensive numerical simulations, we reveal that the growth pattern of the final adoption size versus the information transmission probability is discontinuous and how the final adoption size is affected by hybrid ratio α, global scope control factor ϵ, accumulated received information threshold T, and other parameters on ER network. Furthermore, we give the theory and simulation result on BA network and also compare the two hybrid methods—single infection in one time slice and double infections in one time slice—to evaluate the influence on final adoption size. We found in SIOT hybrid contagion scenario the final adoption size shows the phenomenon of a decline followed by an increase versus different hybrid ratio, and it is both verified in theory and numerical simulation. Through validation by thousands of experiments, our developed theory agrees well with the numerical simulations.http://dx.doi.org/10.1155/2019/6803801
collection DOAJ
language English
format Article
sources DOAJ
author Mingyong Yin
Xingshu Chen
Qixu Wang
Wei Wang
Yulong Wang
spellingShingle Mingyong Yin
Xingshu Chen
Qixu Wang
Wei Wang
Yulong Wang
Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT
Security and Communication Networks
author_facet Mingyong Yin
Xingshu Chen
Qixu Wang
Wei Wang
Yulong Wang
author_sort Mingyong Yin
title Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT
title_short Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT
title_full Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT
title_fullStr Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT
title_full_unstemmed Dynamics on Hybrid Complex Network: Botnet Modeling and Analysis of Medical IoT
title_sort dynamics on hybrid complex network: botnet modeling and analysis of medical iot
publisher Hindawi-Wiley
series Security and Communication Networks
issn 1939-0114
1939-0122
publishDate 2019-01-01
description With the rapid development of Internet of things technology, the application of intelligent devices in the medical industry has become ubiquitous. Connected devices have revolutionized clinicians and patient care but also made modern hospitals vulnerable to cyber attacks. Among the security risks, botnets are of particular concern, which can be used to control thousands of devices for remote data theft and equipment destruction. In this paper, we propose a non-Markovian spread dynamics model to understand the effects of botnet propagation, which can characterize the hybrid contagion situation in reality. Based on the Susceptible-Adopted-Recovered model, we introduce nonredundant memory spread mechanism for global propagation, as a tuner to adjust spreading rate difference. For describing the proposed model, we extend a heterogeneous edge-based compartmental theory. Through extensive numerical simulations, we reveal that the growth pattern of the final adoption size versus the information transmission probability is discontinuous and how the final adoption size is affected by hybrid ratio α, global scope control factor ϵ, accumulated received information threshold T, and other parameters on ER network. Furthermore, we give the theory and simulation result on BA network and also compare the two hybrid methods—single infection in one time slice and double infections in one time slice—to evaluate the influence on final adoption size. We found in SIOT hybrid contagion scenario the final adoption size shows the phenomenon of a decline followed by an increase versus different hybrid ratio, and it is both verified in theory and numerical simulation. Through validation by thousands of experiments, our developed theory agrees well with the numerical simulations.
url http://dx.doi.org/10.1155/2019/6803801
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AT xingshuchen dynamicsonhybridcomplexnetworkbotnetmodelingandanalysisofmedicaliot
AT qixuwang dynamicsonhybridcomplexnetworkbotnetmodelingandanalysisofmedicaliot
AT weiwang dynamicsonhybridcomplexnetworkbotnetmodelingandanalysisofmedicaliot
AT yulongwang dynamicsonhybridcomplexnetworkbotnetmodelingandanalysisofmedicaliot
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