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Social network security using genetic algorithm

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Abstract

This paper introduces a proposed method for hidden community detection using genetic algorithm to consider network immunization against malware propagation. A dynamic spreading model is proposed, namely the susceptible–infected–recovered–susceptible with vaccination and quarantine states (SIRS-QV) to control the speed of malware propagation in communities. The vital nodes in communities are vaccinated to improve immunization of social networks. Moreover, the genetic algorithm is used to discover hidden network communities based on modularity criteria to measure the strength of a set of communities that partition the network. The hiddenness value is calculated to select a community with a higher hiddenness value and vaccinate the nodes in these communities to reduce the rapid spread of malware and after a short time halt the malware in the network.

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Notes

  1. Heterogeneous Mobile Wireless Sensor Networks (HMWSNs).

  2. Internet of Things (IoTs).

  3. Girvan–Newman.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

    Benyamin MazhariSefat & Soodeh Hosseini

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  1. Benyamin MazhariSefat
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  2. Soodeh Hosseini
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Correspondence to Soodeh Hosseini.

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MazhariSefat, B., Hosseini, S. Social network security using genetic algorithm. Evolving Systems 14, 175–190 (2023). https://doi.org/10.1007/s12530-022-09442-4

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  • DOI: https://doi.org/10.1007/s12530-022-09442-4

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