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CC BY V4.0, Open Access
Journal article
Stochastic Modeling of IoT Botnet Spread: A Short Survey on Mobile Malware Spread Modeling
IEEE access, Vol.8, pp.228818-228830
2020
Abstract
The Internet of Things (IoT) devices are being widely deployed and have been targeted and victimized by malware attacks. The mathematical modelling for an accurate prediction of malicious spreads of botnets across IoT networks is of great importance. Suppose the spread of IoT botnets can be predicted using mathematical models, the security community can then take the necessary steps to deter an outbreak of botnet attacks and minimize the damage caused by malware. This paper surveys mobile malware epidemiological models to understand the mechanisms and dynamics of malware spread for IoT botnets. We describe the characteristics of IoT botnets based on the Susceptible-Infection-Recovery-Susceptible and Susceptible-Exposed-Infection-Recovery-Susceptible epidemic models. These models extend the traditional SIR (Susceptible-Infection-Recovery) model by adding extra states and parameters specific to the epidemic spread of IoT botnets. We use mathematical modelling to simulate complex spreading processes of IoT botnets and interpret the influence of an epidemic on distributed denial of service attacks. We use MATLAB and R to illustrate the use of a stochastic IoT botnet transmission model in the identification and mitigation of challenges towards minimizing the impact of devastating IoT botnet epidemics.
Details
- Title
- Stochastic Modeling of IoT Botnet Spread: A Short Survey on Mobile Malware Spread Modeling
- Authors/Creators
- Arash Mahboubi - Charles Sturt Univ, Sch Comp & Math, Port Macquarie, NSW 2444, AustraliaSeyit Camtepe - CSIRO Data61, Marsfield, NSW 2122, AustraliaKeyvan Ansari
- Publication Details
- IEEE access, Vol.8, pp.228818-228830
- Publisher
- IEEE
- Identifiers
- 991005578970007891
- Copyright
- © 2020 IEEE
- Murdoch Affiliation
- College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.1302 Intrusion Detection
- Web Of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications
- ESI research areas
- Engineering