SEI<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e336" altimg="si19.svg"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>RS malware propagation model considering two infection rates in cyber–physical systems

Yu, Zhenhua; Gao, Hongxia; Wang, Dan; Alnuaim, Abeer Ali; Firdausi, Muhammad; Mostafa, Almetwally M.

doi:10.1016/j.physa.2022.127207

Cited by 37 publications

(5 citation statements)

References 32 publications

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“…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.…”

Section: Review Of Literature Modelsmentioning

confidence: 99%

Deriving Exact Mathematical Models of Malware Based on Random Propagation

Carnier,

Li,

Fujimoto

et al. 2024

Mathematics

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The advent of the Internet of Things brought a new age of interconnected device functionality, ranging from personal devices and smart houses to industrial control systems. However, increased security risks have emerged in its wake, in particular self-replicating malware that exploits weak device security. Studies modeling malware epidemics aim to predict malware behavior in essential ways, usually assuming a number of simplifications, but they invariably simplify the single most important subdynamics of malware: random propagation. In our previous work, we derived and presented the first exact mathematical model of random propagation, defined as the subdynamics of propagation of a malware model. The propagation dynamics were derived for the SIS model in discrete form. In this work, we generalize the methodology of derivation and extend it to any Markov chain model of malware based on random propagation. We also propose a second method of derivation based on modifying the simplest form of the model and adjusting it for more complex models. We validated the two methodologies on three malware models, using simulations to confirm the exactness of the propagation dynamics. Stochastic errors of less than 0.2% were found in all simulations. In comparison, the standard nonlinear model of propagation (present in ∼95% of studies) has an average error of 5% and a maximum of 9.88% against simulations. Moreover, our model has a low mathematical trade-off of only two additional operations, being a proper substitute to the standard literature model whenever the dynamical equations are solved numerically.

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Section: Review Of Literature Modelsmentioning

confidence: 99%

Deriving Exact Mathematical Models of Malware Based on Random Propagation

Carnier,

Li,

Fujimoto

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…The main goal of this malware is to collect private data related to companies, such as emails, keyboard keys, and network traffic [72]. Yu et al [73] present a malware propagation model in CPSs, namely SEI 2 RS, which considers two infectious rates. The equilibria are calculated, and the stability, bifurcation of the equilibria are analyzed and proved.…”

Section: Jamming Noisementioning

confidence: 99%

A Survey on Cyber–Physical Systems Security

Gao

Cong

et al. 2023

IEEE Internet Things J.

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“…The explanatory models (the macro-level) can analyze the information dissemination process and predict the population's trends over time using mathematical models [37]. The dynamics model has the advantages of high applicability, fast analysis, high selectivity, and sensitivity, widely applying to the spread of infectious diseases [38,39], computer viruses [40,41], malware [42,43], marketing advertising, and allocation of medical resources [44]. In this section, this paper primarily focuses on the epidemic, network-based structure, and competitive models.…”

Section: The Explanatory Modelmentioning

confidence: 99%

A Survey of Information Dissemination Model, Datasets, and Insight

Liu,

Zhang,

Shi

et al. 2023

Mathematics

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Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the latest models or methods. In this paper, we review information dissemination models from the past three years and conduct a detailed analysis, such as explanatory and predictive models. Moreover, we provide public datasets, evaluation metrics, and interface tools for researchers focusing more on algorithm design and modeling. Finally, we discuss the model application and future research directions. This paper aims to understand better the research progress and development trends for beginners and guide future research endeavors. We believe this article will attract more researchers’ interest and attention to the information dissemination field on social networks.

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SEI2RS malware propagation model considering two infection rates in cyber–physical systems

Cited by 37 publications

References 32 publications

Deriving Exact Mathematical Models of Malware Based on Random Propagation

Deriving Exact Mathematical Models of Malware Based on Random Propagation

A Survey on Cyber–Physical Systems Security

A Survey of Information Dissemination Model, Datasets, and Insight

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