Advanced malware propagation on random complex networks

Rey, Ángel Martín del; Hernández, Guillermo; Tabernero, Arantxa; Dios, Araceli Queiruga

doi:10.1016/j.neucom.2020.03.115

Cited by 15 publications

(5 citation statements)

References 31 publications

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“…Yao et al model and analyse the quarantine strategy with time delay and variable infection rate [28]. Martín del Rey et al propose a malware propagation model based on random complex networks, it is based on individuals and described by cellular automata [29]. Sheng et al study the spread of industrial viruses with intelligent honeynet model [30].…”

Section: Related Workmentioning

confidence: 99%

Modeling and analysis in peer-to-peer botnet with virtual patching and quarantine strategy

Yang,

Fu,

Yao

et al. 2024

Preprint

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Botnets, especially peer-to-peer (P2P) botnets have become the root because of many Internet attacks recently. A novel dynamical model is presented to describe the propagation of P2P botnets which is called SIIR model. To effectively suppress P2P botnets, quarantine and virtual patching strategy are proposed and two dynamical models are given based on the SIIR model (SIIQR model and SIIQPR model). The two models can examine the impact of different containment strategies on the growth of the P2P botnets. In addition, the stability of equilibrium is investigated and the basic reproduction number is obtained which governs whether or not P2P botnets are extinct. The virtual patching strategy and quarantine strategy can effectively contain the propagation of the P2P botnets. Numerical and simulation results show the effectiveness of our models, larger infected rate and larger deploying rate of virtual patching can control the number of infected hosts more effectively.

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Section: Related Workmentioning

confidence: 99%

Modeling and analysis in peer-to-peer botnet with virtual patching and quarantine strategy

Yang,

Fu,

Yao

et al. 2024

Preprint

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“…In [24], an agent-based SEIRD model is described, providing a detailed analysis of all the characteristics of the agents involved in the propagation. In [25], an individual-based model is proposed such that the dynamics of the malware outbreak is governed by means of a cellular automaton. In all these models, the variables involved in the transition functions correspond to the states of the particular devices, in contrast to what happens in global models, where the variables stand for the size of each compartment.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

Martín del Rey

2023

Mathematics

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The main goal of this work is to propose a novel compartmental SEA (Susceptible–Exposed–Attacked) model to simulate malware spreading on an IoT (Internet of Things) network. This is a deterministic and individual-based model, whose main novelty compared to others lies in the used of continuous mathematical techniques, such as ordinary differential equations, in the description of local transition rules that define the changes of the states of the devices. These states are given by probability vectors representing the probabilities of being susceptible, exposed and attacked at each step of time. The qualitative study of the model is presented, and several simulations are performed.

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“…As a result, they provide a detailed representation of the epidemiological state of each device over time, rather than only the global evolution of each compartment. Regrettably, only a handful of such individual-based models have been proposed [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Malware Propagation on Wireless Sensor Networks: A New Approach Using Queueing Theory and HJ-Biplot with a SIRS Model

Frutos-Bernal,

Rodríguez-Rosa,

Anciones-Polo

et al. 2023

Mathematics

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Most research on malware focuses mainly on its detection, without paying attention to its propagation trends. However, modeling the spread of malware is an important research problem because it allows us to predict how malware will evolve and to take steps to prevent its propagation, hence the interest in analyzing this spread from a statistical point of view. This work proposes a malware propagation prediction methodology based on multivariate statistical techniques such as HJ-Biplot in combination with closed queuing networks. Datasets generated using individual-based SIRS models are used to validate the proposed methodology, although any other model could have been chosen to test its validity. Experimental results show that the proposed model can effectively predict and classify malware and discover the influence of different model parameters on the malware propagation situation.

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Advanced malware propagation on random complex networks

Cited by 15 publications

References 31 publications

Modeling and analysis in peer-to-peer botnet with virtual patching and quarantine strategy

Modeling and analysis in peer-to-peer botnet with virtual patching and quarantine strategy

Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

Analyzing Malware Propagation on Wireless Sensor Networks: A New Approach Using Queueing Theory and HJ-Biplot with a SIRS Model

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