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. 2024 Dec 26;19(12):e0301888.
doi: 10.1371/journal.pone.0301888. eCollection 2024.

A cost-effective adaptive repair strategy to mitigate DDoS-capable IoT botnets

Jiamin Hu  1 , Xiaofan Yang  1
Affiliations

A cost-effective adaptive repair strategy to mitigate DDoS-capable IoT botnets

Jiamin Hu et al. PLoS One. .

Abstract

Distributed denial of service (DDoS) is a type of cyberattack in which multiple compromised systems flood the bandwidth or resources of a single system, making the flooded system inaccessible to legitimate users. Since large-scale botnets based on the Internet of Things (IoT) have been hotbeds for launching DDoS attacks, it is crucial to defend against DDoS-capable IoT botnets effectively. In consideration of resource constraints and frequent state changes for IoT devices, they should be equipped with repair measures that are cost-effective and adaptive to mitigate the impact of DDoS attacks. From the mitigation perspective, we refer to the collection of repair costs at all times as a repair strategy. This paper is then devoted to studying the problem of developing a cost-effective and adaptive repair strategy (ARS). First, we establish an IoT botware propagation model that fully captures the state evolution of an IoT network under attack and defense interventions. On this basis, we model the ARS problem as a data-driven optimal control problem, aiming to realize both learning and prediction of propagation parameters based on network traffic data observed at multiple discrete time slots and control of IoT botware propagation to a desired infection level. By leveraging optimal control theory, we propose an iterative algorithm to solve the problem, numerically obtaining the learned time-varying parameters and a repair strategy. Finally, the performance of the learned parameters and the resulting strategy are examined through computer experiments.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Diagram of the IoT network evolutionary model.
Fig 2
Fig 2. Flowchart of the RIR algorithm.
Fig 3
Fig 3. The fitting result of the propagation parameter in Experiment 1.
Fig 4
Fig 4. The fitting result of the expected infected state in Experiment 1.
Fig 5
Fig 5. The control results in Experiment 1.
Fig 6
Fig 6. The fitting result of propagation parameter in Experiment 2.
Fig 7
Fig 7. The fitting result of expected infected state in Experiment 2.
Fig 8
Fig 8. The control results in Experiment 2.
Fig 9
Fig 9. The resulting propagation parameter in Experiment 3.
Fig 10
Fig 10. The fitting result of expected infected state in Experiment 3.
Fig 11
Fig 11. The control results in Experiment 3.
Fig 12
Fig 12. The resulting propagation parameter in Experiment 4.
Fig 13
Fig 13. The fitting result of infection state in Experiment 4.
Fig 14
Fig 14. The control result in Experiment 5.
Fig 15
Fig 15. The control result in Experiment 6.
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References

    1. Greengard S. The internet of things, MIT press, 2021.
    1. Etemadi M, Ghobaei-Arani M, Shahidinejad A. A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach. Cluster Computing. 2021;24(4):3277–92. doi: 10.1007/s10586-021-03307-2 - DOI
    1. Reiss-Mirzaei M, Ghobaei-Arani M, Esmaeili L. A review on the edge caching mechanisms in the mobile edge computing: A social-aware perspective. Internet of Things. 2023:100690. doi: 10.1016/j.iot.2023.100690 - DOI
    1. Khanday SA, Fatima H, Rakesh N. Implementation of intrusion detection model for DDoS attacks in lightweight IoT networks. Expert Systems with Applications. 2023;215:119330. doi: 10.1016/j.eswa.2022.119330 - DOI
    1. Kumari P, Jain AK. A comprehensive study of DDoS attacks over IoT network and their countermeasures. Computers & Security. 2023:103096. doi: 10.1016/j.cose.2023.103096 - DOI

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