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

Objectives. The study sets out to develop a scalable method for detecting multi-vector attacks on Internet of Things (IoT) devices. Given the growth of security threats in IoT networks, such a solution must provide high accuracy in detecting attacks with minimal computing costs while taking into account the resource constraints of IoT devices.
Methods. The developed hybrid neural network architecture combines convolutional networks for spatial dependence analysis and long short-term memory networks or gated recurrent units representing types of recurrent neural networks for analyzing time dependencies in network traffic. Model parameters and computational costs are reduced by pruning. A blockchain with a proof of voting3 consensus mechanism provides secure data management and decentralized verification.
Results. Experiments on the CIC IoT Dataset 20234 showed the effectiveness of the model: the accuracy and F1 measure were 99.1%. This confirms the ability to detect known and new attacks in real time with high accuracy and completeness. Processing time is reduced to 12 ms, while memory usage is reduced to 180 MB, which makes the model suitable for devices with limited resources.
Conclusions. The developed model is superior to analogues in terms of accuracy, processing time, and memory usage. Hybrid architecture, pruning, and decentralized verification provide effectiveness against multi-vector IoT threats.

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