Analysis of network traffic as a means of cyber threat detection
DOI:
https://doi.org/10.15330/itee.2024.1.03Keywords:
cybersecurity, network traffic, intrusion detection, machine learning, neural networks, anomaly detection, PCA, dimensionality reduction, real-time processing, IoTAbstract
The work presents methods and algorithms for detecting cyber threats based on network traffic analysis using machine learning and artificial intelligence techniques. The study evaluates statistical analysis, ensemble models, and neural networks for identifying anomalies and unusual behavior patterns. Emphasis is placed on optimizing computational efficiency while maintaining prediction accuracy. A real-time system architecture for data collection and analysis is proposed.
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