Simulation model of a scalable method for detecting multi-vector attacks taking into account the limitations of computing and information resources of IoT devices

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Simulation model demonstrates 99.1% accuracy in detecting IoT attacks, highlighting efficiency with limited resources.

Developed model achieves 99.1% accuracy and F1 measure, proving effectiveness against multi-vector attacks in IoT.
Processing time is optimized to 12 ms and memory usage to 180 MB, suitable for resource-constrained devices.
Hybrid neural network architecture combines convolutional networks and long short-term memory networks for efficient analysis.
Incorporation of decentralized verification enhances security for IoT data management and verification.

Petrenko et al. (2025) studied this question.

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