MODEL AND METHODS OF DETECTION OF A LARGE-SCALE ATTACK IN THE IOT ENVIRONMENT
DOI:
https://doi.org/10.26906/SUNZ.2024.1.127Keywords:
dataset, neural network, machine learning, network traffic, IDS, training, prediction, anomaly detection, attack, Internet of Things, IoT, intrusion detection systemAbstract
The main concept and subject of the study is the detection of various types of extensive attacks in the IoT infrastructure, an overview of the presented model, methods and existing advanced intrusion detection systems. The purpose of this work is to propose a real-time intrusion detection system that will be trained on a large data set using a neural network using an ensemble machine learning method. The subject of the research is an overview of existing methods and models for detecting a large-scale attack and proposing an intrusion detection system solution, which will be based on the method of detecting anomalies and a neural network. Conclusion. An intrusion detection system was built, which analyzes Internet traffic, extracts signs from the packet, processes them and predicts various types of attacks, as well as characterizes them by type. Security threat can be considered as the main critical issue for IoT devices, so the use of such systems reduces the risks of data loss.Downloads
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