Abstract
IoT technology has been on an uprising these last years, and the number of devices connected to the Internet is likely to keep increasing. As such, the amount of attacks targeting these devices is at an all-time high, and a big percentage of all cyberattacks are focused on IoT devices. This creates the necessity of proposing models to estimate the impact of malware on an IoT network that helps the proposal of countermeasures to protect the network and reduce the possible costs of an attack. In this sense, we propose a stochastic epidemiological SIS model to analyze the behavior of an interconnected network of IoT devices that have been infected by malware. To fulfill this goal, we formulated the initial SIS model, then, implemented this model using Markov Chains, and validated our model by comparing it to the Gillespie simulation algorithm.
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Flórez, J., Montoya, G.A., Lozano-Garzón, C. (2023). An Epidemiological SIS Malware Spreading Model Based on Markov Chains for IoT Networks. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 693. Springer, Singapore. https://doi.org/10.1007/978-981-99-3243-6_53
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DOI: https://doi.org/10.1007/978-981-99-3243-6_53
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